Electric drive assembly NVH (Noise Vibration and Harshness) test system for electric automobile

Abstract

The invention discloses an electric drive assembly NVH test system for an electric automobile, and relates to the field of electric automobiles, the electric drive assembly NVH test system comprises a management center, and the management center is in communication connection with an automobile data acquisition module, an automobile data analysis module, an automobile data processing module and an automobile evaluation optimization module; the automobile data acquisition module is used for acquiring basic parameters, vibration driving parameters and sound driving parameters of the electric drive assembly; the automobile data analysis module is used for establishing an electric automobile driving environment model and carrying out multi-modal data analysis on an electric automobile to obtain vibration state data and sound state data; the automobile data processing module is used for obtaining vibration characteristic parameters and noise multi-factor parameters; and the electric vehicle evaluation and optimization module comprehensively evaluates the electric vehicle according to the vibration characteristic parameters and the noise multi-factor parameters, obtains an evaluation result, optimizes the electric vehicle, improves the comprehensive performance and safety of the electric vehicle, improves the comfort of a driver, and improves the precision of vehicle management.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicles, and in particular to an NVH testing system for an electric drive assembly of an electric vehicle. Background Art

[0002] In recent years, with the increasing prominence of energy and environmental issues, gasoline and diesel-powered vehicles have been subject to certain restrictions and impacts. Electric vehicles can meet the dual requirements of energy and environmental protection. Electric vehicles for various purposes have emerged and become the most ideal and promising green transportation tool to replace fuel vehicles. With the development of science and technology and the economy, people are paying more and more attention to the comfort of vehicles, and the NVH performance of vehicles has become an issue of concern.

[0003] However, in the process of testing the NVH performance of the electric drive assembly, the existing NVH testing technology has a long development cycle, high development cost, large performance test error, and high risk in real electric vehicle driving tests, and it is impossible to accurately test the NVH performance of the electric drive assembly. For this reason, an NVH testing system for an electric drive assembly of an electric vehicle is now provided. Summary of the Invention

[0004] In order to solve the above technical problems, the object of the present invention is to provide an electric drive assembly NVH test system for electric vehicles, including a management center, wherein the management center is communicatively connected to a vehicle data acquisition module, a vehicle data analysis module, a vehicle data processing module, and a vehicle evaluation and optimization module;

[0005] The vehicle data acquisition module is used to collect basic parameters, vibration parameters and sound parameters of the electric drive assembly;

[0006] The vehicle data analysis module is used to establish an electric vehicle driving environment model, perform multimodal data analysis on the electric vehicle based on basic parameters, driving vibration parameters and driving sound parameters, and obtain vibration data and sound data;

[0007] The vehicle data processing module is used to process the vibration state data and the acoustic state data obtained according to the electric vehicle driving environment model, and obtain vibration characteristic parameters and noise multi-factor parameters;

[0008] The electric vehicle evaluation and optimization module comprehensively evaluates the electric vehicle according to the vibration characteristic parameters and the noise multi-factor parameters, obtains the evaluation results, and optimizes the electric vehicle according to the evaluation results.

[0009] Furthermore, the process of the vehicle data acquisition module acquiring basic parameters, vibration drive parameters, and sound drive parameters of the electric drive assembly includes:

[0010] The basic parameters include the structural coefficient and model of the electric drive assembly;

[0011] The vibration driving parameters include acceleration x component, acceleration y component and acceleration z component;

[0012] The sound driving parameters include sound pressure level value and time characteristics;

[0013] Set up car monitoring points;

[0014] The automobile monitoring points include first-class automobile monitoring points and second-class automobile monitoring points;

[0015] The vehicle monitoring points include engine monitoring points, body monitoring points and chassis monitoring points;

[0016] Set up testing cycles;

[0017] According to the test cycle, the vehicle monitoring points collect the basic parameters, vibration parameters and sound parameters of the electric drive assembly in real time.

[0018] Furthermore, the vehicle data analysis module establishes an electric vehicle driving environment model, performs multimodal data analysis on the electric vehicle based on basic parameters, driving vibration parameters, and driving sound parameters, and obtains vibration data and sound data in a process including:

[0019] The electric vehicle driving environment model is provided with an external environment sub-model, a driving sub-model, a simulated driving sub-model, a structural dynamics sub-model and a vehicle maneuvering sub-model;

[0020] The external environment sub-model sets a variety of road conditions and a variety of operating conditions to analyze the driving status of the electric vehicle under a variety of road conditions;

[0021] The driving sub-model is used to analyze the factors affecting the vehicle state caused by the internal environment;

[0022] The structural dynamics sub-model establishes a structural mechanics model of the electric vehicle based on basic parameters;

[0023] The vehicle maneuverability sub-model is used to perform NVH testing on the electric drive assembly of the electric vehicle and output the test results;

[0024] Input the basic parameters, vibration parameters, and sound parameters collected in real time into the electric vehicle driving environment model to conduct NVH testing on the electric drive assembly of the electric vehicle;

[0025] According to the modal analysis method, the natural vibration amplitude and natural frequency are obtained;

[0026] Obtain vibration and acoustic data of electric vehicles based on NVH testing of the electric drive assembly of the vehicle maneuver sub-model;

[0027] The vibration state data are the vibration amplitude, vibration frequency and vibration time of the electric vehicle;

[0028] The acoustic data includes the sound intensity generated by the electric vehicle, the sound intensity of the electric drive assembly, and the time of generation.

[0029] Furthermore, the vehicle data processing module processes the vibration state data obtained according to the electric vehicle driving environment model to obtain vibration characteristic parameters, including:

[0030] According to the vibration amplitude and the natural vibration amplitude, the vibration amplitude change value of the electric vehicle is extracted to obtain the amplitude characteristic value;

[0031] Establish a two-dimensional coordinate system of the amplitude characteristic value of the electric vehicle at the moment of vibration;

[0032] generating an amplitude characteristic change curve according to the obtained amplitude characteristic value;

[0033] According to the vibration frequency and amplitude characteristic change curve of the electric vehicle, it is recorded as the vibration period;

[0034] The number of times the vibration amplitude is equal to the natural vibration amplitude is recorded as the number of stable amplitudes, and the stable amplitude value is obtained according to the number of stable amplitudes and the vibration frequency;

[0035] According to the vibration period and the steady amplitude value, the steady amplitude jump value is obtained;

[0036] The vibration characteristic parameters include steady amplitude value and steady amplitude jump value.

[0037] Furthermore, the vehicle data processing module processes the acoustic data obtained according to the electric vehicle driving environment model to obtain the noise multi-factor parameters, including:

[0038] Based on the acoustic data, a multivariate regression analysis is performed on the sound intensity of the electric vehicle and the electric drive assembly to obtain the electric vehicle noise regression model and the electric drive assembly noise regression model;

[0039] The sound intensity generated by electric vehicles is analyzed based on the electric vehicle noise regression model to obtain the road noise coefficient, idling noise coefficient, tire noise coefficient, wind noise coefficient and steering noise coefficient;

[0040] The noise intensity of the electric drive assembly is analyzed based on the electric drive assembly noise regression model to obtain the motor noise coefficient, reducer noise coefficient, and battery noise coefficient.

[0041] The noise multi-factor parameters include road noise coefficient, idle noise coefficient, tire noise coefficient, wind noise coefficient, steering noise coefficient, motor noise coefficient, reducer noise coefficient and battery noise coefficient.

[0042] Furthermore, the process of the electric vehicle evaluation optimization module evaluating the electric vehicle according to the vibration characteristic parameters includes:

[0043] Obtain vibration assessment value according to steady amplitude value and steady amplitude jump value;

[0044] Analyze the vibration assessment value to obtain the vibration assessment result;

[0045] The vibration evaluation result includes a normal vibration signal and an abnormal vibration signal.

[0046] Furthermore, the process of evaluating electric vehicles according to noise multi-factor parameters by the electric vehicle evaluation optimization module includes:

[0047] According to the road noise coefficient, idling noise coefficient, tire noise coefficient, wind noise coefficient and steering noise coefficient in the electric vehicle noise regression model, the comprehensive noise coefficient is obtained;

[0048] Analyze the comprehensive noise coefficient 1 to obtain the noise evaluation result 1;

[0049] The noise evaluation result 1 includes a normal noise signal 1, an abnormal noise signal 1 and a sub-abnormal noise signal 1;

[0050] Electric vehicles are evaluated based on an electric drivetrain noise regression model.

[0051] Furthermore, it is characterized in that the process of evaluating the electric vehicle according to the electric drive assembly noise regression model includes:

[0052] According to the motor noise coefficient, reducer noise coefficient and battery noise coefficient in the electric drive assembly noise regression model, the motor noise coefficient, reducer noise coefficient and battery noise coefficient are multiplied to obtain the comprehensive noise coefficient 2;

[0053] Noise normal signal 2 and noise abnormal signal 2;

[0054] Based on the vibration evaluation value, comprehensive noise coefficient one and comprehensive noise coefficient two, comprehensive level processing is performed to obtain the comfort evaluation value.

[0055] Compared with the prior art, the beneficial effects of the present invention are: collecting basic parameters, driving vibration parameters and driving sound parameters, obtaining various index parameters of electric vehicles, establishing an electric vehicle driving environment model, simulating the driving conditions of electric vehicles in various environments and the driving experience of the driver, performing electric drive assembly NVH testing through the electric vehicle driving environment model, obtaining vibration data and sound data, analyzing the vibration data, obtaining vibration characteristic parameters, reflecting the vibration performance of the electric drive assembly, performing multivariate regression analysis on the sound data, obtaining noise multi-factor parameters, reflecting the noise-related indicators of various components of the electric vehicle, and evaluating according to the vibration characteristic parameters to generate evaluation results, evaluating according to the noise multi-factor parameters to generate evaluation results, evaluating the driving comfort according to the above evaluation results, obtaining a comfort evaluation value, judging the problem-generating area of ​​the electric drive assembly and the driving experience, and sending the evaluation results to management personnel for optimization management, improving the accuracy of the electric drive assembly NVH test and driving comfort, and adjusting the design of the electric vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic diagram of an electric drive assembly NVH testing system for electric vehicles according to an embodiment of the present application. DETAILED DESCRIPTION

[0057] like Figure 1 As shown, an electric drive assembly NVH test system for electric vehicles includes a management center, which is communicatively connected to a vehicle data acquisition module, a vehicle data analysis module, a vehicle data processing module, and a vehicle evaluation and optimization module;

[0058] The vehicle data acquisition module is used to collect basic parameters, vibration parameters and sound parameters of the electric drive assembly;

[0059] The vehicle data analysis module is used to establish an electric vehicle driving environment model, perform multimodal data analysis on the electric vehicle based on basic parameters, driving vibration parameters and driving sound parameters, and obtain vibration data and sound data;

[0060] The vehicle data processing module is used to process the vibration state data and the acoustic state data obtained according to the electric vehicle driving environment model, and obtain vibration characteristic parameters and noise multi-factor parameters;

[0061] The electric vehicle evaluation and optimization module comprehensively evaluates the electric vehicle according to the vibration characteristic parameters and the noise multi-factor parameters, obtains the evaluation results, and optimizes the electric vehicle according to the evaluation results.

[0062] It should be further explained that, in a specific implementation, the process of the vehicle data acquisition module for collecting basic parameters, vibration drive parameters, and sound drive parameters of the electric drive assembly includes:

[0063] The basic parameters include the structural coefficient and model of the electric drive assembly;

[0064] It should be further explained that, in the specific implementation process, the basic parameters specifically include the design structure and model of the electric motor, electronic controller, transmission, battery pack, electrical connection and wiring, mechanical structure and support for these structures;

[0065] The vibration driving parameters include acceleration x component, acceleration y component and acceleration z component;

[0066] The sound driving parameters include sound pressure level value and time characteristics;

[0067] Set up car monitoring points;

[0068] The automobile monitoring points include first-class automobile monitoring points and second-class automobile monitoring points;

[0069] It should be further explained that, in the specific implementation process, a vibration sensor is installed in the first type of vehicle monitoring point to measure the vibration-related data generated during the driving of the electric vehicle, and an acoustic sensor is installed in the second type of vehicle monitoring point to measure the noise-related data generated during the driving of the electric vehicle;

[0070] The vehicle monitoring points include engine monitoring points, body monitoring points and chassis monitoring points;

[0071] It should be further explained that, in the specific implementation process, the vehicle monitoring point is set in the electric drive assembly structure, the engine monitoring point is specifically set inside the engine, the body monitoring point is specifically set in the tires, windows, doors, seats, engine, exhaust pipe and electric vehicle parts, and the chassis monitoring point is specifically set in the chassis;

[0072] Set the test period t;

[0073] According to the test cycle t, the vehicle monitoring points collect the basic parameters, driving vibration parameters and driving sound parameters of the electric drive assembly in real time.

[0074] It should be further explained that, in a specific implementation process, the vehicle data analysis module is used to establish an electric vehicle driving environment model, and perform multimodal data analysis on the electric vehicle based on basic parameters, driving vibration parameters, and driving sound parameters. The process of obtaining vibration data and sound data includes:

[0075] The electric vehicle driving environment model is provided with an external environment sub-model, a driving sub-model, a simulated driving sub-model, a structural dynamics sub-model and a vehicle maneuvering sub-model;

[0076] The external environment sub-model sets a variety of road conditions and a variety of operating conditions to analyze the driving status of the electric vehicle under a variety of road conditions;

[0077] It should be further explained that, in the specific implementation process, multiple road conditions refer to ambient temperature, ambient humidity, wind level and direction, road smoothness, and road roughness; multiple operating conditions refer to the speed, acceleration, load, rapid acceleration, deceleration, start and stop of the electric vehicle;

[0078] The driving sub-model is used to analyze the factors affecting the vehicle state caused by the internal environment;

[0079] The structural dynamics sub-model establishes a structural mechanics model of the electric vehicle based on basic parameters;

[0080] The vehicle maneuverability sub-model is used to perform NVH testing on the electric drive assembly of the electric vehicle and output the test results;

[0081] It should be further explained that, in a specific implementation process, the electric vehicle driving environment model is used to simulate the external environment during the electric vehicle driving process, the internal environment of the electric vehicle during the electric vehicle driving process, the electric vehicle driver experience, the structural composition of the electric vehicle, and the maneuvering state of the electric vehicle during the driving process;

[0082] It should be further explained that, in the specific implementation process, the electric vehicle driving environment model is specifically a model that simulates various types of road conditions and environments for electric vehicles, monitors the maneuverability of the vehicle, and conducts NVH tests on the electric drive assembly of the electric vehicle under various conditions;

[0083] Input the basic parameters, vibration parameters, and sound parameters collected in real time into the electric vehicle driving environment model to conduct NVH testing on the electric drive assembly of the electric vehicle;

[0084] The electric vehicle driving environment model is analyzed through modal analysis to obtain the natural vibration amplitude and natural frequency;

[0085] Through the vehicle maneuvering sub-model, the electric drive assembly NVH test of the electric vehicle is carried out to obtain the vibration and acoustic data of the electric vehicle;

[0086] The vibration state data are the vibration amplitude, vibration frequency and vibration time of the electric vehicle;

[0087] The acoustic data includes the sound intensity of the electric vehicle, the sound intensity of the electric drive assembly, and the time of generation;

[0088] It should be further explained that, in the specific implementation process, the sound intensity generated by the electric vehicle is the comprehensive sound intensity of the sound intensity generated by the electric drive assembly, wind noise, tire noise, steering noise, etc., and the sound intensity generated by the electric vehicle is obtained at the driver and passenger seats.

[0089] It should be further explained that, in a specific implementation, the vehicle data processing module is used to separately process the vibration state data and the acoustic state data obtained according to the electric vehicle driving environment model, and the process of obtaining the vibration characteristic parameters and the noise multi-factor parameters includes:

[0090] Conduct vibration analysis on electric vehicles based on their natural vibration amplitude, natural frequency and vibration state data;

[0091] According to the vibration amplitude and the natural vibration amplitude, the vibration amplitude change value of the electric vehicle is extracted to obtain the amplitude characteristic value;

[0092] It should be further explained that, in the specific implementation process, the specific process of extracting the vibration amplitude change value of the electric vehicle is as follows: according to the natural vibration amplitude and the difference between the vibration amplitudes at the same time, the vibration amplitude change value of the electric vehicle is obtained, and the vibration amplitude change value includes positive, zero and negative values. When the vibration amplitude change value is zero, it means that the vibration amplitude of the electric vehicle is equal to the natural vibration amplitude;

[0093] Establish a two-dimensional coordinate system of the amplitude characteristic value of the electric vehicle at the moment of vibration;

[0094] generating an amplitude characteristic change curve according to the obtained amplitude characteristic value;

[0095] Mapping the generated amplitude characteristic change curve into a two-dimensional coordinate system;

[0096] Conduct periodic analysis of electric vehicles based on the amplitude characteristic change curve and the vibration amplitude of the electric vehicle;

[0097] According to the vibration frequency of the electric vehicle, the amplitude characteristic change curve is divided into several segments, which are recorded as vibration cycles;

[0098] Obtain the vibration times and amplitude characteristic values ​​in each vibration cycle, count the number of times the amplitude characteristic value is equal to 0, record it as the number of stable amplitude times, calculate the ratio of the stable amplitude times to the vibration times, and obtain the stable amplitude value;

[0099] According to the vibration period, the difference between the stable amplitude values ​​corresponding to adjacent vibration periods is used to calculate the stable amplitude change to obtain the stable amplitude jump value;

[0100] The vibration characteristic parameters include steady amplitude value and steady amplitude jump value;

[0101] Based on the acoustic data, a multivariate regression analysis is performed on the sound intensity of the electric vehicle and the electric drive assembly to obtain the electric vehicle noise regression model and the electric drive assembly noise regression model;

[0102] In the electric vehicle noise regression model, the sound intensity generated by the electric vehicle is used as the dependent variable, and road noise, idling noise, tire noise, wind noise and steering noise are used as independent variables;

[0103] In the electric drive assembly noise regression model, the sound intensity generated by the electric drive assembly is used as the dependent variable, and the motor noise, reducer noise and battery noise are used as independent variables;

[0104] The sound intensity generated by electric vehicles is analyzed through the electric vehicle noise regression model to obtain the road noise coefficient, idle noise coefficient, tire noise coefficient, wind noise coefficient and steering noise coefficient;

[0105] The noise intensity of the electric drive assembly is analyzed using the electric drive assembly noise regression model to obtain the motor noise coefficient, reducer noise coefficient, and battery noise coefficient.

[0106] The noise multi-factor parameters include road noise coefficient, idle noise coefficient, tire noise coefficient, wind noise coefficient, steering noise coefficient, motor noise coefficient, reducer noise coefficient and battery noise coefficient.

[0107] It should be further explained that, in a specific implementation process, the electric vehicle evaluation and optimization module is used to comprehensively evaluate the electric vehicle based on the vibration characteristic parameters and the noise multi-factor parameters, obtain the evaluation results, and optimize the electric vehicle based on the evaluation results. The process includes:

[0108] According to the steady amplitude value and the steady amplitude jump value, the vibration assessment of the electric vehicle is carried out to obtain the vibration assessment result;

[0109] The steady amplitude value and the steady amplitude jump value are summed to obtain the vibration evaluation value;

[0110] When the vibration evaluation value is less than or equal to 1, a vibration normal signal is generated;

[0111] When the vibration evaluation value is greater than 1, a vibration abnormality signal is generated;

[0112] The vibration assessment result includes a normal vibration signal and an abnormal vibration signal;

[0113] Conduct electric vehicle noise analysis based on the electric vehicle noise regression model and the electric drive assembly noise regression model to obtain noise assessment results;

[0114] According to the road noise coefficient, idling noise coefficient, tire noise coefficient, wind noise coefficient and steering noise coefficient in the electric vehicle noise regression model, the road noise coefficient, idling noise coefficient, tire noise coefficient, wind noise coefficient and steering noise coefficient are multiplied to obtain a comprehensive noise coefficient of one;

[0115] When the comprehensive noise coefficient is less than 1, a noise normal signal is generated;

[0116] When the comprehensive noise coefficient is greater than or equal to 1, a noise abnormality signal is generated;

[0117] According to the abnormal noise signal 1, the road noise coefficient, idling noise coefficient, tire noise coefficient, wind noise coefficient and steering noise coefficient are analyzed separately;

[0118] When the road noise coefficient, the idle noise coefficient, the tire noise coefficient, the wind noise coefficient, or the steering noise coefficient is greater than 1, generating a noise abnormality signal 1, and associating the coefficient greater than 1 with the noise abnormality signal 1;

[0119] According to the motor noise coefficient, reducer noise coefficient and battery noise coefficient in the electric drive assembly noise regression model, the motor noise coefficient, reducer noise coefficient and battery noise coefficient are multiplied to obtain the comprehensive noise coefficient 2;

[0120] When the comprehensive noise coefficient 2 is less than 1, a noise normal signal 2 is generated;

[0121] When the comprehensive noise coefficient 2 is greater than or equal to 1, a noise abnormality signal 2 is generated;

[0122] According to the second abnormal noise signal, the noise coefficient of the motor, the noise coefficient of the reducer and the noise coefficient of the battery are analyzed respectively;

[0123] When the motor noise coefficient, the reducer noise coefficient, or the battery noise coefficient is greater than 1, a second sub-noise abnormality signal is generated, and the coefficient greater than 1 is associated with the second sub-noise abnormality signal;

[0124] The noise evaluation result includes a normal noise signal 1, an abnormal noise signal 1, a sub-abnormal noise signal 1, a normal noise signal 2, and an abnormal noise signal 2;

[0125] Comprehensively analyze the vibration and noise evaluation results to obtain comfort evaluation results;

[0126] Perform comprehensive level processing based on the vibration assessment value, comprehensive noise coefficient 1 and comprehensive noise coefficient 2 to obtain the comfort assessment value;

[0127] It should be further explained that, in a specific implementation process, the specific process of the comprehensive level processing includes: summing the result of multiplying the vibration evaluation value by the comprehensive noise coefficient 1 and the result of multiplying the vibration evaluation value by the comprehensive noise coefficient 2, and performing a difference calculation between the obtained sum and the result of multiplying the comprehensive noise coefficient 1 by the comprehensive noise coefficient 2 to obtain a comfort evaluation value;

[0128] The evaluation results are sent to managers, who then check and optimize the performance and comfort of the electric vehicle based on the evaluation results.

[0129] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An electric drive assembly NVH test system for electric vehicles, including a management center, characterized in that: The management center is communicatively connected to a vehicle data acquisition module, a vehicle data analysis module, a vehicle data processing module, and a vehicle evaluation and optimization module; The vehicle data acquisition module is used to collect basic parameters, vibration parameters and sound parameters of the electric drive assembly; The vehicle data analysis module is used to establish an electric vehicle driving environment model, perform multimodal data analysis on the electric vehicle based on basic parameters, driving vibration parameters and driving sound parameters, and obtain vibration data and sound data; The vehicle data processing module is used to process the vibration state data and the acoustic state data obtained according to the electric vehicle driving environment model, and obtain vibration characteristic parameters and noise multi-factor parameters; The electric vehicle evaluation and optimization module comprehensively evaluates the electric vehicle according to the vibration characteristic parameters and the noise multi-factor parameters, obtains the evaluation results, and optimizes the electric vehicle according to the evaluation results.

2. The NVH test system for an electric drive assembly of an electric vehicle according to claim 1, characterized in that: The process of the vehicle data acquisition module collecting basic parameters, vibration parameters and sound parameters of the electric drive assembly includes: The basic parameters include the structural coefficient and model of the electric drive assembly; The vibration driving parameters include acceleration x component, acceleration y component and acceleration z component; The sound driving parameters include sound pressure level value and time characteristics; Set up car monitoring points; The automobile monitoring points include first-class automobile monitoring points and second-class automobile monitoring points; The vehicle monitoring points include engine monitoring points, body monitoring points and chassis monitoring points; Set up testing cycles; According to the test cycle, the vehicle monitoring points collect the basic parameters, vibration parameters and sound parameters of the electric drive assembly in real time.

3. The NVH test system for an electric drive assembly of an electric vehicle according to claim 2, characterized in that: The vehicle data analysis module establishes an electric vehicle driving environment model, performs multimodal data analysis on the electric vehicle based on basic parameters, driving vibration parameters, and driving sound parameters, and obtains vibration data and sound data in the following process: The electric vehicle driving environment model is provided with an external environment sub-model, a driving sub-model, a simulated driving sub-model, a structural dynamics sub-model and a vehicle maneuvering sub-model; The external environment sub-model sets a variety of road conditions and a variety of operating conditions to analyze the driving status of the electric vehicle under a variety of road conditions; The driving sub-model is used to analyze the factors affecting the vehicle state caused by the internal environment; The structural dynamics sub-model establishes a structural mechanics model of the electric vehicle based on basic parameters; The vehicle maneuverability sub-model is used to perform NVH testing on the electric drive assembly of the electric vehicle and output the test results; Input the basic parameters, vibration parameters, and sound parameters collected in real time into the electric vehicle driving environment model to conduct NVH testing on the electric drive assembly of the electric vehicle; According to the modal analysis method, the natural vibration amplitude and natural frequency are obtained; Obtain vibration and acoustic data of electric vehicles based on NVH testing of the electric drive assembly of the vehicle's motor sub-model; The vibration state data are the vibration amplitude, vibration frequency and vibration time of the electric vehicle; The acoustic data includes the sound intensity generated by the electric vehicle, the sound intensity of the electric drive assembly, and the time of generation.

4. The NVH test system for an electric drive assembly of an electric vehicle according to claim 3, characterized in that: The vehicle data processing module processes the vibration state data obtained according to the electric vehicle driving environment model to obtain the vibration characteristic parameters, including: According to the vibration amplitude and the natural vibration amplitude, the vibration amplitude change value of the electric vehicle is extracted to obtain the amplitude characteristic value; Establish a two-dimensional coordinate system of the amplitude characteristic value of the electric vehicle at the moment of vibration; generating an amplitude characteristic change curve according to the obtained amplitude characteristic value; According to the vibration frequency and amplitude characteristic change curve of the electric vehicle, it is recorded as the vibration period; The number of times the vibration amplitude is equal to the natural vibration amplitude is recorded as the number of stable amplitudes, and the stable amplitude value is obtained according to the number of stable amplitudes and the vibration frequency; According to the vibration period and the steady amplitude value, the steady amplitude jump value is obtained; The vibration characteristic parameters include steady amplitude value and steady amplitude jump value.

5. The NVH test system for an electric drive assembly of an electric vehicle according to claim 4, characterized in that: The vehicle data processing module processes the acoustic data obtained according to the electric vehicle driving environment model to obtain the noise multi-factor parameters, including: Based on the acoustic data, a multivariate regression analysis is performed on the sound intensity of the electric vehicle and the electric drive assembly to obtain the electric vehicle noise regression model and the electric drive assembly noise regression model; The sound intensity generated by electric vehicles is analyzed based on the electric vehicle noise regression model to obtain the road noise coefficient, idling noise coefficient, tire noise coefficient, wind noise coefficient and steering noise coefficient; The noise intensity of the electric drive assembly is analyzed based on the electric drive assembly noise regression model to obtain the motor noise coefficient, reducer noise coefficient, and battery noise coefficient. The noise multi-factor parameters include road noise coefficient, idle noise coefficient, tire noise coefficient, wind noise coefficient, steering noise coefficient, motor noise coefficient, reducer noise coefficient and battery noise coefficient.

6. The NVH testing system for an electric drive assembly of an electric vehicle according to claim 5, characterized in that: The process of the electric vehicle evaluation optimization module evaluating the electric vehicle according to the vibration characteristic parameters includes: Obtain vibration assessment value according to steady amplitude value and steady amplitude jump value; Analyze the vibration assessment value to obtain the vibration assessment result; The vibration evaluation result includes a normal vibration signal and an abnormal vibration signal.

7. The NVH testing system for an electric drive assembly of an electric vehicle according to claim 6, characterized in that: The process of the electric vehicle evaluation optimization module evaluating the electric vehicle according to the noise multi-factor parameters includes: According to the road noise coefficient, idling noise coefficient, tire noise coefficient, wind noise coefficient and steering noise coefficient in the electric vehicle noise regression model, the comprehensive noise coefficient is obtained; Analyze the comprehensive noise coefficient 1 to obtain the noise evaluation result 1; The noise evaluation result 1 includes a normal noise signal 1, an abnormal noise signal 1 and a sub-abnormal noise signal 1; Electric vehicles are evaluated based on an electric drivetrain noise regression model.

8. The NVH testing system for an electric drive assembly of an electric vehicle according to claim 7, characterized in that: The process of evaluating electric vehicles based on the electric drivetrain noise regression model includes: According to the motor noise coefficient, reducer noise coefficient and battery noise coefficient in the electric drive assembly noise regression model, the motor noise coefficient, reducer noise coefficient and battery noise coefficient are multiplied to obtain the comprehensive noise coefficient 2; Noise normal signal 2 and noise abnormal signal 2; Based on the vibration evaluation value, comprehensive noise coefficient one and comprehensive noise coefficient two, comprehensive level processing is performed to obtain the comfort evaluation value.

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