New energy electric vehicle detection method and system based on quartz crystal oscillator

Abstract

The invention discloses a new energy electric vehicle detection method and system based on a quartz crystal oscillator, and particularly relates to the field of new energy electric vehicles, and the method comprises the steps of data region division, data acquisition, data analysis and early warning. Through fine data region division and comprehensive data acquisition, deep understanding and accurate evaluation of the performance of the new energy electric vehicle and application of a data analysis model and a comprehensive analysis model are realized, the accuracy and reliability of evaluation are improved, an early warning system is established, abnormal conditions of the electric vehicle can be found in time, driving safety is ensured, and the safety of the electric vehicle is improved. The implementation of the invention is helpful for improving the detection efficiency and the maintenance quality of the new energy electric vehicle and reducing the use risk and the maintenance cost, and is of great significance for promoting the development of the new energy electric vehicle industry.

Description

Technical Field

[0001] The present invention relates to the field of new energy electric vehicles, and more specifically, to a new energy electric vehicle detection method and system based on a quartz crystal oscillator. Background Art

[0002] New energy electric vehicles are a key development direction in the current automotive industry, and improving their performance testing and maintenance technologies is crucial. Research in this area not only involves battery management, vibration analysis, control feedback, and powertrain systems, but also directly impacts the safety, reliability, and service life of electric vehicles. Therefore, developing an efficient and accurate new energy electric vehicle testing system is crucial for ensuring electric vehicle performance and improving the user experience.

[0003] Existing technology primarily relies on traditional sensor data collection and simple data analysis methods. This involves real-time monitoring of various tram parameters and uploading the data to a central processing unit for analysis, enabling a preliminary assessment of tram performance. While this process can provide some insight into tram performance, the depth and breadth of data processing still needs to be improved.

[0004] However, existing technologies still have many shortcomings in practical applications. For one thing, traditional sensors can be affected by environmental factors during data collection, resulting in reduced data accuracy. Furthermore, simple data analysis methods fail to fully reflect the actual performance of electric vehicles and identify potential problems and faults. Therefore, there is an urgent need to develop a more advanced and efficient new energy electric vehicle detection system. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a new energy electric vehicle detection method and system based on a quartz crystal oscillator, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a new energy electric vehicle detection method based on a quartz crystal oscillator, comprising:

[0007] Step 1: Data area division: The target data area is determined by dividing the target new energy electric vehicle detection data into sub-data areas according to the mileage of the target new energy electric vehicle, and marked as 1, 2, ..., n in sequence;

[0008] Step 2: Data Collection: This is used to collect battery and energy management data, micro-vibration and noise analysis data, stability and control feedback data, and power system characteristic data of the target new energy electric vehicle;

[0009] Step 3: Data analysis: This is used to analyze the data collected in step 2, including battery and energy management data analysis models, micro-vibration and noise analysis data analysis models, stability and control feedback data analysis models, and power system characteristic data analysis models;

[0010] Step 4: Comprehensive analysis: used to establish a comprehensive analysis model, conduct a comprehensive analysis of the data after data analysis, and calculate the comprehensive abnormality index of the target new energy electric vehicle;

[0011] Step 5: Early warning: used to establish a preset value of the comprehensive abnormality index, judge the status of the target new energy electric vehicle based on the preset value of the comprehensive abnormality index, and issue an early warning signal based on the judgment result.

[0012] Preferably, the battery and energy management data include the battery ion concentration gradient change rate, the battery thermal conductivity change coefficient, the battery volume fraction thermal effect and the dynamic voltage hysteresis coefficient, which are marked as CG, CV, CF and CD respectively; the micro-vibration and noise analysis data include high-frequency resonance mode, low-frequency common mode vibration increment, noise interference mode analysis and vibration energy density distribution function, which are marked as HF, HL, HM and HV respectively; the stability and control feedback data include control system uncertainty measurement, real-time dynamic response sensitivity, yaw rate deviation integral and lane keeping error function, which are marked as DS, DR, DY and DL respectively; the power system characteristic data include flux link strength change, power output jitter spectrum, torque harmonic content ratio and reducer engagement impact coefficient, which are marked as FL, FP, FT and FG respectively.

[0013] Preferably, the battery ion concentration gradient change rate is monitored in real time inside the battery by sensors and electrochemical analysis equipment, the battery thermal conductivity change coefficient is measured by experimentally testing the battery under different temperature environments using laser thermal reflection technology, the battery volume fraction thermal effect is evaluated by using X-ray tomography combined with thermal analysis instruments to evaluate volume change and temperature effect, and the dynamic voltage hysteresis coefficient is recorded by using a high-precision voltage and current recorder during multiple charge and discharge cycles.

[0014] Preferably, the high-frequency resonant mode is subjected to frequency scanning and modal analysis by using a laser Doppler vibrometer, the low-frequency common-mode vibration increment is subjected to data acquisition and analysis by an accelerometer and a vibration analyzer, the noise interference mode is analyzed by measuring under different working conditions using a sound level meter and a spectrum analyzer, and the vibration energy density distribution function is obtained by combining finite element analysis with actual testing to obtain the vibration energy distribution.

[0015] Preferably, the control system uncertainty metric is evaluated through system identification and simulation of the robust control algorithm, the real-time dynamic response sensitivity is monitored in real time by combining an embedded system with a high-frequency data acquisition module, the yaw rate deviation integral is recorded by a gyroscope and a multi-axis sensor during a driving test, and the lane keeping error function obtains deviation data through a visual recognition system and a GPS combined with a posture tracking device.

[0016] Preferably, the change in the flux link strength is measured by a high-precision torque sensor and a flux meter when the motor is running, the power output jitter spectrum is analyzed by a power analyzer combined with a fast Fourier transform to analyze the output characteristics, the torque harmonic content ratio is tested and calculated by a motor test platform and a harmonic analyzer, and the reducer engagement impact coefficient is collected by acoustic vibration analysis technology and transient dynamic analysis tools.

[0017] Preferably, the battery and energy management data analysis model is used to analyze the battery and energy management data, and is specifically expressed as follows: C i represents the battery and energy management effect value of the i-th sub-data area, CG i Indicates the battery ion concentration gradient change rate of the i-th sub-data area, CV i Represents the battery thermal conductivity variation coefficient of the i-th sub-data area, CF i represents the thermal effect of the battery volume fraction in the ith sub-data region, CD i represents the dynamic voltage hysteresis coefficient of the i-th sub-data region, and n represents the number of sub-data regions.

[0018] Preferably, the micro-vibration and noise analysis data analysis model is used to analyze the micro-vibration and noise analysis data, and is specifically expressed as follows: H i represents the micro-vibration and noise evaluation value of the i-th sub-data area, HF i represents the high-frequency resonance mode of the ith sub-data region, HL i Represents the low-frequency common mode vibration increment of the ith sub-data region, HM i represents the noise interference pattern analysis of the ith sub-data region, HV i Represents the vibration energy density distribution function of the i-th sub-data region.

[0019] Preferably, the stability and control feedback data analysis model is used to analyze the stability and control feedback data, and is specifically expressed as follows: D i represents the stability and control feedback evaluation value of the i-th sub-data region, DS i represents the uncertainty measure of the control system in the ith sub-data region, DRi Indicates the real-time dynamic response sensitivity of the ith sub-data area, DY i represents the yaw rate deviation integral of the i-th sub-data region, DL i represents the lane keeping error function of the i-th sub-data region.

[0020] Preferably, the power system characteristic data analysis model is used to analyze the power system characteristic data, and is specifically expressed as: F i represents the power system characteristic evaluation value of the i-th sub-data area, FL i Indicates the change in the magnetic flux link strength of the i-th sub-data area, FP i It represents the power output jitter spectrum of the ith sub-data area, FT i Indicates the torque harmonic content ratio of the ith sub-data region, FG i Indicates the reducer meshing impact coefficient of the i-th sub-data area.

[0021] Preferably, the comprehensive analysis model is specifically expressed as: η represents the comprehensive abnormality index of the target new energy electric vehicle, w1 represents the weight of the battery and energy management data, w2 represents the weight of the micro-vibration and noise analysis data, w3 represents the weight of the stability and control feedback data, w4 represents the weight of the power system characteristic data, and λ represents other influencing factors of the comprehensive abnormality index.

[0022] Preferably, the preset value of the comprehensive abnormality index is specifically expressed as η0. When η0≥η, it means that there is no abnormality in the target new energy electric vehicle, and the detection of the target new energy electric vehicle is maintained. When η0<η, it means that the target new energy electric vehicle is abnormal, and an early warning signal is sent to the user control terminal.

[0023] Preferably, a new energy electric vehicle detection system based on a quartz crystal oscillator comprises:

[0024] Data area division module: used to determine the target new energy electric vehicle detection data as the target data area, and divide the target data area into various sub-data areas according to the mileage of the target new energy electric vehicle, and mark them as 1, 2...n in sequence;

[0025] Data acquisition module: used to collect battery and energy management data, micro-vibration and noise analysis data, stability and control feedback data, and power system characteristic data of the target new energy electric vehicle;

[0026] Data analysis module: used to analyze the data collected by the data acquisition module, including the battery and energy management data analysis unit, the micro-vibration and noise analysis data analysis unit, the stability and control feedback data analysis unit, and the power system characteristics data analysis unit;

[0027] Comprehensive analysis module: used to establish a comprehensive analysis model, conduct comprehensive analysis on the data after data analysis, and calculate the comprehensive abnormality index of the target new energy electric vehicle;

[0028] Early warning module: used to establish a preset value of the comprehensive abnormality index, judge the status of the target new energy electric vehicle based on the preset value of the comprehensive abnormality index, and issue an early warning signal based on the judgment result.

[0029] The technical effects and advantages of the present invention are as follows:

[0030] The present invention divides the target new energy electric vehicle detection data into sub-data areas according to the mileage through data area division, which can manage and analyze data more finely. This division helps to identify problems that may occur in the electric vehicle at different driving stages, and provides a basis for subsequent data collection and analysis; through data collection, the battery and energy management data, micro-vibration and noise analysis data, stability and control feedback data and power system characteristic data of the target new energy electric vehicle are collected. These data contain key information on many aspects such as the electric vehicle's performance, structure, stability and power system. The comprehensiveness and accuracy of data collection provide strong support for subsequent data analysis and comprehensive evaluation, which helps to timely discover potential problems and faults of the electric vehicle and improve detection efficiency and accuracy; through data analysis, a battery and energy management data analysis model, micro-vibration and noise analysis data, stability and control feedback data and power system characteristic data are established. Acoustic analysis data analysis models, etc., can conduct in-depth analysis of the collected data, extract key information, and provide data support for the comprehensive evaluation of the performance of trams. The data analysis model can comprehensively consider multiple factors to improve the accuracy and reliability of the evaluation; through comprehensive analysis to establish a comprehensive analysis model, the data of each data area can be comprehensively analyzed to calculate the comprehensive abnormality index of the target new energy tram. The comprehensive abnormality index can reflect the overall performance status of the tram, help to timely discover potential problems, and provide guidance for repair and maintenance; through early warning, a preset value of the comprehensive abnormality index is established, and an early warning signal is issued according to the judgment result, which can timely discover the abnormal situation of the tram and remind users or maintenance personnel to take measures to avoid the occurrence or expansion of faults. The early warning system can improve the safety and reliability of trams and reduce maintenance costs and usage risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of the method structure of the present invention.

[0032] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] refer to Figure 1 A new energy electric vehicle detection method based on a quartz crystal oscillator is shown, and the specific steps include:

[0035] Step 1: Data area division: used to determine the target new energy electric vehicle detection data as the target data area, and divide the target data area into various sub-data areas by dividing it according to the mileage of the target new energy electric vehicle, and mark them as 1, 2...n in sequence.

[0036] Step 2: Data acquisition: used to collect battery and energy management data, micro-vibration and noise analysis data, stability and control feedback data, and power system characteristic data of the target new energy electric vehicle.

[0037] The battery and energy management data include the battery ion concentration gradient change rate, the battery thermal conductivity change coefficient, the battery volume fraction thermal effect and the dynamic voltage hysteresis coefficient, which are marked as CG, CV, CF and CD respectively. The micro-vibration and noise analysis data include high-frequency resonance mode, low-frequency common mode vibration increment, noise interference mode analysis and vibration energy density distribution function, which are marked as HF, HL, HM and HV respectively. The stability and control feedback data include control system uncertainty measurement, real-time dynamic response sensitivity, yaw rate deviation integral and lane keeping error function, which are marked as DS, DR, DY and DL respectively. The power system characteristic data includes flux link strength change, power output jitter spectrum, torque harmonic content ratio and reducer engagement impact coefficient, which are marked as FL, FP, FT and FG respectively.

[0038] The battery ion concentration gradient change rate is monitored in real time inside the battery by sensors and electrochemical analysis equipment. The battery thermal conductivity change coefficient is measured by experimentally testing the battery under different temperature environments using laser thermal reflection technology. The battery volume fraction thermal effect is evaluated by using X-ray tomography combined with thermal analysis instruments to evaluate volume change and temperature effect. The dynamic voltage hysteresis coefficient is recorded by using a high-precision voltage and current recorder during multiple charge and discharge cycles.

[0039] The high-frequency resonance mode is subjected to frequency scanning and modal analysis by using a laser Doppler vibrometer, the low-frequency common-mode vibration increment is subjected to data acquisition and analysis by an accelerometer and a vibration analysis instrument, the noise interference mode analysis is measured under different working conditions by using a sound level meter and a spectrum analyzer, and the vibration energy density distribution function is obtained by combining finite element analysis with actual testing to obtain the vibration energy distribution.

[0040] The control system uncertainty measurement is evaluated through system identification and simulation of the robust control algorithm. The real-time dynamic response sensitivity is monitored in real time by combining an embedded system with a high-frequency data acquisition module. The yaw rate deviation integral is recorded by a gyroscope and a multi-axis sensor during driving tests. The lane keeping error function obtains deviation data through a visual recognition system and GPS combined with a posture tracking device.

[0041] The change in the flux link strength is measured by a high-precision torque sensor and a flux meter when the motor is running. The power output jitter spectrum is analyzed by a power analyzer combined with fast Fourier transform to analyze the output characteristics. The torque harmonic content ratio is tested and calculated by a motor test platform and a harmonic analyzer. The speed reducer engagement impact coefficient is collected using acoustic vibration analysis technology and transient dynamic analysis tools.

[0042] Step 3: Data analysis: used to analyze the data collected in step 2, including battery and energy management data analysis model, micro-vibration and noise analysis data analysis model, stability and control feedback data analysis model, and power system characteristic data analysis model.

[0043] The battery and energy management data analysis model is used to analyze battery and energy management data, and is specifically expressed as follows: C i represents the battery and energy management effect value of the i-th sub-data area, CG i Indicates the battery ion concentration gradient change rate of the i-th sub-data area, CV i Represents the battery thermal conductivity variation coefficient of the i-th sub-data area, CF i represents the thermal effect of the battery volume fraction in the ith sub-data region, CD i represents the dynamic voltage hysteresis coefficient of the i-th sub-data region, and n represents the number of sub-data regions.

[0044] The micro-vibration and noise analysis data analysis model is used to analyze the micro-vibration and noise analysis data, and is specifically expressed as follows: H i represents the micro-vibration and noise evaluation value of the i-th sub-data area, HF i represents the high-frequency resonance mode of the ith sub-data region, HL iRepresents the low-frequency common mode vibration increment of the ith sub-data region, HM i represents the noise interference pattern analysis of the ith sub-data region, HV i Represents the vibration energy density distribution function of the i-th sub-data region.

[0045] The stability and control feedback data analysis model is used to analyze the stability and control feedback data, and is specifically expressed as follows: D i represents the stability and control feedback evaluation value of the i-th sub-data region, DS i represents the uncertainty measure of the control system in the ith sub-data region, DR i Indicates the real-time dynamic response sensitivity of the ith sub-data area, DY i represents the yaw rate deviation integral of the i-th sub-data region, DL i represents the lane keeping error function of the i-th sub-data region.

[0046] The power system characteristic data analysis model is used to analyze the power system characteristic data, and is specifically expressed as follows: F i represents the power system characteristic evaluation value of the i-th sub-data area, FL i Indicates the change in the magnetic flux link strength of the i-th sub-data area, FP i It represents the power output jitter spectrum of the ith sub-data area, FT i Indicates the torque harmonic content ratio of the ith sub-data region, FG i Indicates the reducer meshing impact coefficient of the i-th sub-data area.

[0047] Step 4: Comprehensive analysis: used to establish a comprehensive analysis model, conduct comprehensive analysis on the data after data analysis, and calculate the comprehensive abnormality index of the target new energy electric vehicle.

[0048] The comprehensive analysis model is specifically expressed as follows: η represents the comprehensive abnormality index of the target new energy electric vehicle, w1 represents the weight of the battery and energy management data, w2 represents the weight of the micro-vibration and noise analysis data, w3 represents the weight of the stability and control feedback data, w4 represents the weight of the power system characteristic data, and λ represents other influencing factors of the comprehensive abnormality index.

[0049] Step 5: Early warning: used to establish a preset value of the comprehensive abnormality index, judge the status of the target new energy electric vehicle based on the preset value of the comprehensive abnormality index, and issue an early warning signal based on the judgment result.

[0050] The preset value of the comprehensive abnormality index is specifically expressed as η0. When η0≥η, it means that there is no abnormality in the target new energy electric vehicle, and the detection of the target new energy electric vehicle is maintained. When η0<η, it means that the target new energy electric vehicle is abnormal, and an early warning signal is sent to the user control terminal.

[0051] refer to Figure 2 , a new energy electric vehicle detection system based on quartz crystal oscillator, including:

[0052] Data area division module: used to determine the target new energy electric vehicle detection data as the target data area, and divide the target data area into various sub-data areas according to the mileage of the target new energy electric vehicle, and mark them as 1, 2...n in sequence;

[0053] Data acquisition module: used to collect battery and energy management data, micro-vibration and noise analysis data, stability and control feedback data, and power system characteristic data of the target new energy electric vehicle;

[0054] Data analysis module: used to analyze the data collected by the data acquisition module, including the battery and energy management data analysis unit, the micro-vibration and noise analysis data analysis unit, the stability and control feedback data analysis unit, and the power system characteristics data analysis unit;

[0055] Comprehensive analysis module: used to establish a comprehensive analysis model, conduct comprehensive analysis on the data after data analysis, and calculate the comprehensive abnormality index of the target new energy electric vehicle;

[0056] Early warning module: used to establish a preset value of the comprehensive abnormality index, judge the status of the target new energy electric vehicle based on the preset value of the comprehensive abnormality index, and issue an early warning signal based on the judgment result.

[0057] The present invention divides the target new energy electric vehicle detection data into sub-data areas according to the mileage through data area division, which can manage and analyze data more finely. This division helps to identify problems that may occur in the electric vehicle at different driving stages, and provides a basis for subsequent data collection and analysis; through data collection, the battery and energy management data, micro-vibration and noise analysis data, stability and control feedback data and power system characteristic data of the target new energy electric vehicle are collected. These data contain key information on many aspects such as the electric vehicle's performance, structure, stability and power system. The comprehensiveness and accuracy of data collection provide strong support for subsequent data analysis and comprehensive evaluation, which helps to timely discover potential problems and faults of the electric vehicle and improve detection efficiency and accuracy; through data analysis, a battery and energy management data analysis model, micro-vibration and noise analysis data, stability and control feedback data and power system characteristic data are established. Acoustic analysis data analysis models, etc., can conduct in-depth analysis of the collected data, extract key information, and provide data support for the comprehensive evaluation of the performance of trams. The data analysis model can comprehensively consider multiple factors to improve the accuracy and reliability of the evaluation; through comprehensive analysis to establish a comprehensive analysis model, the data of each data area can be comprehensively analyzed to calculate the comprehensive abnormality index of the target new energy tram. The comprehensive abnormality index can reflect the overall performance status of the tram, help to timely discover potential problems, and provide guidance for repair and maintenance; through early warning, a preset value of the comprehensive abnormality index is established, and an early warning signal is issued according to the judgment result, which can timely discover the abnormal situation of the tram and remind users or maintenance personnel to take measures to avoid the occurrence or expansion of faults. The early warning system can improve the safety and reliability of trams and reduce maintenance costs and usage risks.

[0058] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict.

[0059] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A new energy electric vehicle detection method based on quartz crystal oscillator, characterized in that: include: Step 1: Data area division: The target data area is determined by dividing the target new energy electric vehicle detection data into sub-data areas according to the mileage of the target new energy electric vehicle, and marked as 1, 2, ..., n in sequence; Step 2: Data Collection: This is used to collect battery and energy management data, micro-vibration and noise analysis data, stability and control feedback data, and power system characteristic data of the target new energy electric vehicle; Step 3: Data analysis: This is used to analyze the data collected in step 2, including battery and energy management data analysis models, micro-vibration and noise analysis data analysis models, stability and control feedback data analysis models, and power system characteristic data analysis models; Step 4: Comprehensive analysis: used to establish a comprehensive analysis model, conduct a comprehensive analysis of the data after data analysis, and calculate the comprehensive abnormality index of the target new energy electric vehicle; Step 5: Early warning: used to establish a preset value of the comprehensive abnormality index, judge the status of the target new energy electric vehicle based on the preset value of the comprehensive abnormality index, and issue an early warning signal based on the judgment result.

2. The quartz crystal oscillator-based new energy electric vehicle detection method according to claim 1 is characterized in that: The battery and energy management data include the battery ion concentration gradient change rate, the battery thermal conductivity change coefficient, the battery volume fraction thermal effect and the dynamic voltage hysteresis coefficient, which are marked as CG, CV, CF and CD respectively. The micro-vibration and noise analysis data include high-frequency resonance mode, low-frequency common mode vibration increment, noise interference mode analysis and vibration energy density distribution function, which are marked as HF, HL, HM and HV respectively. The stability and control feedback data include control system uncertainty measurement, real-time dynamic response sensitivity, yaw rate deviation integral and lane keeping error function, which are marked as DS, DR, DY and DL respectively. The power system characteristic data includes flux link strength change, power output jitter spectrum, torque harmonic content ratio and reducer engagement impact coefficient, which are marked as FL, FP, FT and FG respectively.

3. The new energy electric vehicle detection method based on quartz crystal oscillator according to claim 1 is characterized in that: The battery and energy management data analysis model is used to analyze battery and energy management data, and is specifically expressed as follows: C i represents the battery and energy management effect value of the i-th sub-data area, CG i Indicates the battery ion concentration gradient change rate of the i-th sub-data area, CV i Represents the battery thermal conductivity variation coefficient of the i-th sub-data area, CF i represents the thermal effect of the battery volume fraction in the ith sub-data region, CD i represents the dynamic voltage hysteresis coefficient of the i-th sub-data region, and n represents the number of sub-data regions.

4. The new energy electric vehicle detection method based on quartz crystal oscillator according to claim 1 is characterized in that: The micro-vibration and noise analysis data analysis model is used to analyze the micro-vibration and noise analysis data, and is specifically expressed as follows: H i represents the micro-vibration and noise evaluation value of the i-th sub-data area, HF i represents the high-frequency resonance mode of the ith sub-data region, HL i Represents the low-frequency common mode vibration increment of the ith sub-data region, HM i represents the noise interference pattern analysis of the ith sub-data region, HV i Represents the vibration energy density distribution function of the i-th sub-data region.

5. The new energy electric vehicle detection method based on quartz crystal oscillator according to claim 1 is characterized in that: The stability and control feedback data analysis model is used to analyze the stability and control feedback data, and is specifically expressed as follows: D i represents the stability and control feedback evaluation value of the i-th sub-data region, DS i represents the uncertainty measure of the control system in the ith sub-data region, DR i Indicates the real-time dynamic response sensitivity of the ith sub-data area, DY i represents the yaw rate deviation integral of the i-th sub-data region, DL i represents the lane keeping error function of the i-th sub-data region.

6. The new energy electric vehicle detection method based on quartz crystal oscillator according to claim 1 is characterized in that: The power system characteristic data analysis model is used to analyze the power system characteristic data, and is specifically expressed as follows: F i represents the power system characteristic evaluation value of the i-th sub-data area, FL i Indicates the change in the magnetic flux link strength of the i-th sub-data area, FP i It represents the power output jitter spectrum of the ith sub-data area, FT i Indicates the torque harmonic content ratio of the ith sub-data region, FG i Indicates the reducer meshing impact coefficient of the i-th sub-data area.

7. The new energy electric vehicle detection method based on quartz crystal oscillator according to claim 1 is characterized in that: The comprehensive analysis model is specifically expressed as follows: η represents the comprehensive abnormality index of the target new energy electric vehicle, w1 represents the weight of the battery and energy management data, w2 represents the weight of the micro-vibration and noise analysis data, w3 represents the weight of the stability and control feedback data, w4 represents the weight of the power system characteristic data, and λ represents other influencing factors of the comprehensive abnormality index.

8. The new energy electric vehicle detection method based on quartz crystal oscillator according to claim 1 is characterized in that: The preset value of the comprehensive abnormality index is specifically expressed as η0. When η0≥η, it means that there is no abnormality in the target new energy electric vehicle, and the detection of the target new energy electric vehicle is maintained. When η0<η, it means that the target new energy electric vehicle is abnormal, and an early warning signal is sent to the user control terminal.

9. A new energy electric vehicle detection system based on a quartz crystal oscillator, according to a new energy electric vehicle detection method based on a quartz crystal oscillator according to any one of claims 1 to 8, characterized in that: include: Data area division module: used to determine the target new energy electric vehicle detection data as the target data area, and divide the target data area into various sub-data areas according to the mileage of the target new energy electric vehicle, and mark them as 1, 2...n in sequence; Data acquisition module: used to collect battery and energy management data, micro-vibration and noise analysis data, stability and control feedback data, and power system characteristic data of the target new energy electric vehicle; Data analysis module: used to analyze the data collected by the data acquisition module, including the battery and energy management data analysis unit, the micro-vibration and noise analysis data analysis unit, the stability and control feedback data analysis unit, and the power system characteristics data analysis unit; Comprehensive analysis module: used to establish a comprehensive analysis model, conduct comprehensive analysis on the data after data analysis, and calculate the comprehensive abnormality index of the target new energy electric vehicle; Early warning module: used to establish a preset value of the comprehensive abnormality index, judge the status of the target new energy electric vehicle based on the preset value of the comprehensive abnormality index, and issue an early warning signal based on the judgment result.

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