A method and system for testing insulation performance of electric vehicles based on big data

Abstract

The present invention discloses a method and system for testing the insulation performance of electric vehicles based on big data, and relates to the technical field of electric vehicle insulation performance testing. The method for testing the insulation performance of electric vehicles based on big data comprises the following steps: accurate evaluation of test sampling, first insulation resistance error judgment, second insulation resistance error judgment, and dynamic insulation fault evaluation judgment. The present invention determines whether to dynamically adjust the resistance based on the results of accurate evaluation of test sampling, then determines whether to perform dynamic insulation fault evaluation based on the insulation resistance error after dynamically adjusting the resistance, and optimizes the dynamic insulation performance test based on the results of the dynamic insulation fault evaluation. Finally, it determines whether to perform test sampling sample optimization based on the confidence interval of the test sampling data, thereby improving the accuracy of the electric vehicle insulation performance test and solving the problem of low accuracy of the electric vehicle insulation performance test caused by sampling data error in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle insulation performance testing, and in particular to a method and system for testing the insulation performance of an electric vehicle based on big data. Background Art

[0002] With growing global attention to environmental protection and energy efficiency, electric vehicles (EVs), as an important alternative to traditional fuel-powered vehicles, have seen widespread adoption and rapid development. EV electrical systems, including battery packs, electric motors, inverters, and other components, face significant safety risks due to their high voltage and high energy density. Insulation performance is a crucial factor in ensuring safe operation within EV electrical systems. Deterioration in insulation performance can lead to serious safety hazards such as electrical failures, battery short circuits, and even fires. Therefore, testing and monitoring EV insulation performance has become increasingly important.

[0003] Existing technologies can conduct detailed monitoring of various components of electric vehicles through real-time data collection and analysis, conduct intelligent prediction and diagnosis based on historical data and real-time data, timely detect potential faults and insulation performance problems, and improve the efficiency and accuracy of insulation performance detection.

[0004] For example, the patent application with publication number CN115754476A discloses a method for measuring the insulation performance of an electric vehicle, including: a resistance mechanism, including a resistor R2, a resistor R1 and a resistor Rs, wherein the resistors R2, R1 and Rs are resistors with dynamically controllable resistance values, the resistor R1 is connected to the positive electrode of the high-voltage DC output, and the resistor R2 is connected to the negative electrode of the high-voltage DC output; the oversampling processing circuit includes a filter conditioning circuit and a microprocessor with a built-in analog-to-digital converter; the output end of the operational amplifier A1 is connected to the input end of the filter conditioning circuit, and the output end of the filter conditioning circuit is connected to the microprocessor cable, and a resistance network is constructed in conjunction with the dynamically controllable resistors, by dynamically controlling and adjusting the resistance value of each resistor in the resistance network.

[0005] For example, an invention patent announcement with announcement number CN113009227B discloses an insulation detection method for an electric vehicle, including: step S10, a charging pile obtains a vehicle insulation resistance protection range of the electric vehicle; step S20, measuring an initial pile insulation value through a pile insulation tester, and calculating upper and lower limit loading resistance values ​​based on the initial pile insulation value and the vehicle insulation resistance protection range; step S30, loading the upper and lower limit loading resistance values ​​on the charging circuit to verify the vehicle insulation resistance protection range; step S40, alternately performing insulation detection through pile and vehicle insulation testers to obtain pile insulation value and vehicle insulation value; step S50, obtaining an insulation error value based on the pile insulation value and the vehicle insulation value, and drawing corresponding curves based on the insulation error value, pile insulation value and vehicle insulation value respectively; step S60, detecting the insulation performance based on the curve.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, due to differences in the service life of the resistance elements, insufficient resolution or accuracy of the analog-to-digital converter, and load changes (such as battery charging and discharging, load changes during motor startup and operation, etc.), there may be deviations between the actual resistance value of the resistor and the standard value, subtle changes in the input signal cannot be accurately captured, and fluctuations in current and voltage in the circuit, thereby affecting the measured insulation resistance value, which may lead to inaccurate test results and fail to truly reflect the insulation status of the electric vehicle's high-voltage system; therefore, after adjusting the resistance in the electric vehicle's high-voltage system, the adjustable resistor switching time may lag behind the high-voltage transient change, and the distributed capacitance and the dynamic resistance network may form a resonant circuit, resulting in measurement signal distortion, and may also introduce electromagnetic interference, resulting in missed detection of rapid insulation faults. There is a problem of low accuracy in electric vehicle insulation performance testing due to sampling data errors. Summary of the Invention

[0008] The embodiments of the present application solve the problem of low accuracy of electric vehicle insulation performance testing caused by sampling data errors in the prior art by providing an electric vehicle insulation performance testing method and system based on big data, thereby improving the accuracy of electric vehicle insulation performance testing.

[0009] An embodiment of the present application provides an electric vehicle insulation performance testing method based on big data, comprising the following steps: S1, performing a first insulation performance pre-test on a preset electric vehicle sampling sample during a pre-sampling period to obtain first test sampling data, and at the same time performing a test sampling accuracy evaluation based on the test sampling accuracy evaluation parameters of the first insulation performance pre-test process to determine whether to dynamically adjust the resistance; S2, if the resistance is not dynamically adjusted, obtaining a first insulation resistance value by injecting a preset DC voltage into the electric vehicle high-voltage system to obtain a first insulation resistance error, and determining whether to perform test sampling sample optimization based on the confidence interval of the first test sampling data; if the test sampling sample is not optimized, performing an insulation performance test on the preset electric vehicle sampling sample, otherwise performing an insulation performance test after the test sampling sample is optimized; S3, if the resistance is dynamically adjusted, injecting a preset DC voltage into the electric vehicle high-voltage system to obtain a first insulation resistance error. A second insulation resistance value obtained by injecting a preset DC voltage into the high-voltage system of the electric vehicle obtains a second insulation resistance error, and based on the second insulation resistance error, it is determined whether to perform a dynamic insulation fault assessment; if not, a second insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain second test sampling data, and then, based on the confidence interval of the second test sampling data, it is determined whether to perform test sampling sample optimization; otherwise, S4 is executed; S4, if a dynamic insulation fault assessment is performed, a dynamic insulation performance test optimization is performed according to the result of the dynamic insulation fault assessment, and then a third insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain third test sampling data, and then, based on the confidence interval of the third test sampling data, it is determined whether to perform test sampling sample optimization; if not, an insulation performance test is performed on the preset electric vehicle sampling sample; otherwise, an insulation performance test is performed after the test sampling sample is optimized.

[0010] An embodiment of the present application provides a system for an electric vehicle insulation performance testing method based on big data, comprising: a test sampling accuracy evaluation module, a first insulation resistance value error judgment module, a second insulation resistance value error judgment module and a dynamic insulation fault evaluation judgment module; wherein the test sampling accuracy evaluation module is used to perform a first insulation performance pre-test on a preset electric vehicle sampling sample during a pre-sampling period to obtain a first test sampling data, and at the same time perform a test sampling accuracy evaluation based on the test sampling accuracy evaluation parameters of the first insulation performance pre-test process to determine whether to dynamically adjust the resistance; the first insulation resistance value error judgment module is used to obtain a first insulation resistance value error by injecting a preset DC voltage into the electric vehicle high-voltage system if the resistance is not dynamically adjusted, and determine whether to perform test sampling sample optimization based on the confidence interval of the first test sampling data; if the test sampling sample optimization is not performed, perform an insulation performance test on the preset electric vehicle sampling sample, otherwise perform an insulation performance test after the test sampling sample optimization; the second insulation resistance value error judgment module is used to determine whether to perform test sampling sample optimization based on the confidence interval of the first test sampling data; if the test sampling sample optimization is not performed, perform an insulation performance test on the preset electric vehicle sampling sample, otherwise perform an insulation performance test after the test sampling sample optimization; The insulation resistance error judgment module is used to obtain a second insulation resistance error by injecting a preset DC voltage into the high-voltage system of the electric vehicle after the dynamic resistance adjustment to obtain a second insulation resistance value, and judge whether to perform dynamic insulation fault assessment based on the second insulation resistance error. If not, a second insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain second test sampling data, and then judge whether to perform test sampling sample optimization based on the confidence interval of the second test sampling data. Otherwise, the function of the dynamic insulation fault assessment judgment module is executed; the dynamic insulation fault assessment judgment module is used to perform a dynamic insulation fault assessment, and then perform a third insulation performance pre-test on the preset electric vehicle sampling sample to obtain third test sampling data according to the result of the dynamic insulation fault assessment to judge whether to perform test sampling sample optimization based on the confidence interval of the third test sampling data. If not, an insulation performance test is performed on the preset electric vehicle sampling sample. Otherwise, an insulation performance test is performed after the test sampling sample is optimized.

[0011] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0012] 1. Determine whether to dynamically adjust the resistance based on the results of accurate test sampling evaluation. Then, after dynamically adjusting the resistance, determine whether to perform a dynamic insulation fault evaluation based on the insulation resistance value error. Perform dynamic insulation performance test optimization based on the results of the dynamic insulation fault evaluation. Finally, determine whether to perform test sampling sample optimization based on the confidence interval of the test sampling data, thereby reducing the error of the test sampling data and improving the accuracy of the electric vehicle insulation performance test. This effectively solves the problem of low accuracy of electric vehicle insulation performance test caused by sampling data error in the existing technology.

[0013] 2. By adjusting the resistance value of the series resistor in the resonant circuit to the resonant resistance value, and then optimizing the resistance adjustment time, the cutoff frequency of the filter is set to the optimized cutoff frequency. At the same time, the inductance value of the common-mode inductor and the capacitance value of the differential-mode capacitor are adjusted in the power line and signal line of the electric vehicle. Finally, the operating frequency of the electric vehicle insulation performance test is adjusted to the adjusted output frequency, thereby ensuring that the electric vehicle maintains a good insulation state under different working conditions, thereby achieving improved accuracy of the electric vehicle insulation performance test.

[0014] 3. By judging whether the average sampling accuracy evaluation index is greater than the preset sampling accuracy evaluation threshold, if the average sampling accuracy evaluation index is greater than the preset sampling accuracy evaluation threshold, the average sampling accuracy evaluation index, the preset input signal extreme value of the analog-to-digital converter, and the voltage withstand voltage deviation coefficient are input and adjusted to obtain the optimized resistance value. The resistance value of the controllable resistor in the preset electric vehicle sampling sample high-voltage system is adjusted to the optimized resistance value, thereby ensuring that the electric vehicle insulation performance test is maintained in the optimal state, thereby achieving improved accuracy of the insulation performance test. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of a method for testing the insulation performance of an electric vehicle based on big data provided in an embodiment of the present application;

[0016] Figure 2 A flow chart of dynamic insulation fault assessment, judgment, and optimization provided in an embodiment of the present application;

[0017] Figure 3 A schematic structural diagram of an electric vehicle insulation performance testing system based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The embodiment of the present application solves the problem of low accuracy of electric vehicle insulation performance testing caused by sampling data errors in the prior art by providing an electric vehicle insulation performance testing method and system based on big data. The test sampling is accurately evaluated through the test sampling accurate evaluation parameters of the first insulation performance pre-test process to determine whether to dynamically adjust the resistance. Then, after the dynamic adjustment of the resistance, the second insulation resistance value error is obtained according to the second insulation resistance value and the preset insulation resistance value to determine whether to perform a dynamic insulation fault evaluation. Finally, after the dynamic insulation performance test is optimized according to the result of the dynamic insulation fault evaluation, it is determined whether to perform test sampling sample optimization based on the confidence interval of the third test sampling data obtained from the third insulation performance pre-test, thereby achieving improved accuracy in the electric vehicle insulation performance test.

[0019] The technical solution in the embodiments of the present application is to solve the problem of low accuracy in the insulation performance test of electric vehicles caused by sampling data errors. The overall idea is as follows:

[0020] The results of accurate test sampling evaluation are used to determine whether to dynamically adjust the resistance. Then, after dynamically adjusting the resistance, it is determined whether to perform a dynamic insulation fault evaluation based on the insulation resistance error. Dynamic insulation performance test optimization is performed based on the results of the dynamic insulation fault evaluation. Finally, based on the confidence interval of the test sampling data, it is determined whether to perform test sampling sample optimization, thereby improving the accuracy of electric vehicle insulation performance testing.

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0022] An embodiment of the present application provides an electric vehicle insulation performance testing method based on big data, comprising the following steps: S1, test sampling accuracy evaluation: performing a first insulation performance pre-test on a preset electric vehicle sampling sample during a pre-sampling period to obtain first test sampling data, and at the same time performing a test sampling accuracy evaluation based on the test sampling accuracy evaluation parameters of the first insulation performance pre-test process to determine whether to dynamically adjust the resistance; the pre-sampling period is set according to a preset person, for example, set to 1 hour; the preset electric vehicle sampling sample represents an initial sample set by the preset person.

[0023] S2, first insulation resistance error judgment: if dynamic resistance adjustment is not performed, a first insulation resistance value is obtained by injecting a preset DC voltage into the electric vehicle high-voltage system, and the first insulation resistance value and the preset insulation resistance value are subjected to deviation processing to obtain a first insulation resistance error. Based on the first insulation resistance error, it is determined whether feedback is performed, and based on the confidence interval of the first test sampling data, it is determined whether test sampling sample optimization is performed. If test sampling sample optimization is not performed, an insulation performance test is performed on the preset electric vehicle sampling sample. Otherwise, an insulation performance test is performed on the preset electric vehicle sampling sample after the test sampling sample optimization. The preset DC voltage is set according to the GB / T 18384 standard, and the injected DC voltage is required to be twice the maximum operating DC voltage of the electric vehicle high-voltage system. The specific formula for the insulation resistance error is: , where JWC represents the insulation resistance error, and JYZ represents the insulation resistance value. Represents a preset insulation resistance value; inputting the first insulation resistance value into the specific formula of the insulation resistance value error to obtain the first insulation resistance value error; inputting the average value and standard deviation of the insulation resistance value in the first test sampling data into the calculation formula of the confidence interval to obtain the confidence interval; the specific calculation formula of the confidence interval is: ,in, is the average value of insulation resistance, is the standard deviation of the insulation resistance value, n is the number of samples of the preset electric vehicle sampling samples in the first test sampling data, is the Z value corresponding to the confidence level under the normal distribution. For a 95% confidence level, the corresponding Z value is 1.96.

[0024] S3, second insulation resistance error judgment: if dynamic resistance adjustment is performed, then after the dynamic resistance adjustment, a preset DC voltage is injected into the high-voltage system of the electric vehicle after the dynamic resistance adjustment to obtain a second insulation resistance value, and the second insulation resistance value and the preset insulation resistance value are subjected to deviation processing to obtain a second insulation resistance value error. Based on the second insulation resistance value error, it is judged whether to perform dynamic insulation fault assessment. If dynamic insulation fault assessment is not performed, a second insulation performance pre-test is performed on a preset electric vehicle sampling sample to obtain second test sampling data. Based on the confidence interval of the second test sampling data, it is judged whether to perform test sampling sample optimization. Otherwise, execute S4; input the second insulation resistance value into the specific formula of the insulation resistance value error to obtain the second insulation resistance value error; input the average value and standard deviation of the insulation resistance values ​​in the second test sampling data into the calculation formula of the confidence interval to obtain the corresponding confidence interval.

[0025] S4, dynamic insulation fault assessment judgment: if a dynamic insulation fault assessment is performed, then after performing dynamic insulation performance test optimization based on the results of the dynamic insulation fault assessment, a third insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain third test sampling data, and then based on the confidence interval of the third test sampling data, it is determined whether to perform test sampling sample optimization; if the test sampling sample optimization is not performed, an insulation performance test is performed on the preset electric vehicle sampling sample; otherwise, an insulation performance test is performed on the preset electric vehicle sampling sample after the test sampling sample optimization; the third insulation resistance value is input into the specific formula of the insulation resistance value error to obtain the third insulation resistance value error; the test sampling data includes the first test sampling data, the second test sampling data and the third test sampling data, all including but not limited to insulation resistance value, voltage, and current.

[0026] In this embodiment, if Figure 1As shown, it is a flow chart of an electric vehicle insulation performance testing method based on big data provided by an embodiment of the present application. The test sampling accuracy evaluation is performed according to the test sampling accuracy evaluation parameters of the first insulation performance pre-test process, and it is judged whether to dynamically adjust the resistance. If the resistance is not dynamically adjusted, it is judged whether to perform test sampling sample optimization based on the confidence interval of the first test sampling data, and the insulation performance test is performed; if the resistance is dynamically adjusted, it is judged whether to perform dynamic insulation fault evaluation based on the second insulation resistance resistance error. If the dynamic insulation fault evaluation is not performed, a second insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain the second test sampling data, and then it is judged whether to perform test sampling sample optimization based on the confidence interval of the second test sampling data. Otherwise, it is judged whether to perform test sampling sample optimization based on the confidence interval of the third test sampling data, and the insulation performance test is performed.

[0027] Due to differences in the age of resistor components, the actual resistance value of a resistor may deviate from the standard value. Insufficient resolution or accuracy of analog-to-digital converters may prevent subtle changes in the input signal from being accurately captured. Load changes (such as those during battery charging and discharging, or motor startup and operation) can cause current and voltage fluctuations in the circuit, affecting the measured insulation resistance value. This can lead to inaccurate test results and a failure to truly reflect the insulation condition of the high-voltage system. Errors during the measurement process may also be amplified. Consequently, after adjusting the resistance, the adjustable resistor switching time may lag behind the high-voltage transient change, leading to missed detection of rapid insulation faults. Distributed capacitance and the dynamic resistor network form a resonant circuit, causing measurement signal distortion. Dynamic resistor switching introduces electromagnetic interference, affecting the normal operation of other electronic systems (such as the Controller Area Network (CAN) bus).

[0028] This application improves the accuracy of test sampling data through dynamic resistance adjustment and multiple pre-tests; determines whether to optimize samples through confidence intervals to ensure the balance of electric vehicle sampling samples; achieves the accuracy of electric vehicle insulation performance testing; and effectively identifies abnormal conditions in the dynamic resistance adjustment process through dynamic insulation fault assessment, thereby improving the effectiveness of electric vehicle insulation performance testing and better reflecting the actual insulation conditions of electric vehicles.

[0029] The real-time collection of test sampling data, accurate evaluation parameters of test sampling and dynamic insulation fault parameters through big data technology is conducive to timely identification of abnormal situations in the data; by analyzing the data in the historical database through big data technology, a mapping set between different parameters can be established and preset values ​​can be obtained, so as to more accurately and timely judge the problems in the insulation performance test process of electric vehicles.

[0030] Furthermore, a test sampling accuracy evaluation is performed based on the test sampling accuracy evaluation parameters of the first insulation performance pre-test process to determine whether to dynamically adjust the resistance. The specific method is as follows:

[0031] A1, based on the insulation resistance value of the preset electric vehicle sampling sample and the preset resistance value obtained from the preset database, the resistance tolerance coefficient is obtained by relative deviation processing, that is, , where DS represents the resistance tolerance coefficient, DSZ represents the resistance value of the preset electric vehicle sampling sample, and is represented by the average value of the resistance value obtained by the resistance measuring instrument in the electric vehicle during the preset sampling period; Indicates the preset resistance value, which is set by the preset person.

[0032] A2, based on the relative deviation processing of the input signal amplitude of the analog-to-digital converter in the preset electric vehicle sampling sample and the preset input signal amplitude obtained from the preset database, the input signal amplitude deviation coefficient is obtained, that is, , where FD represents the input signal amplitude deviation coefficient, FDP represents the input signal amplitude of the analog-to-digital converter in the preset electric vehicle sampling sample, and the amplitude of the actual input signal is obtained through the analog-to-digital converter; Indicates the preset input signal amplitude, which is set by the preset personnel.

[0033] A3 introduces a sampling accuracy assessment compensation factor to assign a coupling process to the resistance tolerance coefficient, the input signal amplitude deviation coefficient, and the denormalized voltage standard deviation, and performs an inverse proportional operation to obtain a sampling accuracy assessment index. The sampling accuracy assessment index is used to quantitatively assess the sampling accuracy in the insulation performance pre-test caused by resistance differences. The sampling accuracy assessment compensation factor includes a first sampling accuracy assessment compensation factor, a second sampling accuracy assessment compensation factor, and a third sampling accuracy assessment compensation factor. The voltage standard deviation is represented by the standard deviation of the voltage obtained by the voltage sensor in the electric vehicle within a preset sampling period.

[0034] The sampling accuracy assessment compensation factors involved are obtained from a preset database. The first sampling accuracy assessment compensation factor represents the degree of influence of the resistance value of the resistor in the electric vehicle on the sampling accuracy assessment index. The second sampling accuracy assessment compensation factor represents the degree of influence of the input signal amplitude of the analog-to-digital converter in the electric vehicle on the sampling accuracy assessment index. The third sampling accuracy assessment compensation factor represents the degree of influence of the voltage standard deviation on the sampling accuracy assessment index. The sum of the three is 1. For example, the resistance value in the electric vehicle and the preset first sampling accuracy assessment compensation factor form a resistance mapping set, and the real-time resistance value in the electric vehicle is input into the resistance mapping set to obtain the corresponding first sampling accuracy assessment compensation factor; the input signal amplitude of the analog-to-digital converter in the electric vehicle and the preset second sampling accuracy assessment compensation factor form an analog-to-digital signal amplitude mapping set, and the real-time input signal amplitude of the analog-to-digital converter in the electric vehicle is input into the analog-to-digital signal amplitude mapping set to obtain the corresponding second sampling accuracy assessment compensation factor; the voltage standard deviation and the preset third sampling accuracy assessment compensation factor form a voltage standard deviation mapping set, and the real-time voltage standard deviation is input into the voltage standard deviation mapping set to obtain the corresponding third sampling accuracy assessment compensation factor. The mapping relationship can be one-to-one or many-to-one.

[0035] Among them, the specific restriction expression of the sampling accuracy evaluation index is: ;

[0036] Where CZ represents the sampling accuracy evaluation index of the pre-sampling period, UBC represents the voltage standard deviation, represents the first sample accurate evaluation compensation factor, represents the second sampling accurate evaluation compensation factor, Denotes the third sample accurate evaluation compensation factor.

[0037] The specific process for determining whether to dynamically adjust resistance is as follows: Determine whether the average sampling accuracy assessment index is greater than a preset sampling accuracy assessment threshold obtained from a preset database. The average sampling accuracy assessment index represents the average of the sampling accuracy assessment indexes of all preset electric vehicle samples, indicating the average sampling accuracy of the insulation performance pre-test. If the average sampling accuracy assessment index is greater than the preset sampling accuracy assessment threshold obtained from the preset database, the resistance is dynamically adjusted. The preset sampling accuracy assessment threshold is represented by the average of the sampling accuracy assessment indexes over a historical time period. If the average sampling accuracy assessment index is not greater than the preset sampling accuracy assessment threshold obtained from the preset database, the electric vehicle insulation performance test continues.

[0038] Among them, dynamically adjusting the resistance means adjusting the resistance value of the controllable resistor in the high-voltage system of the preset electric vehicle sampling sample to the optimized resistance value. The optimized resistance value is obtained by inputting the average sampling accuracy evaluation index, the preset input signal extreme value of the analog-to-digital converter, and the voltage withstand voltage deviation coefficient into the adjusted resistance value mapping set. The adjusted resistance value mapping set is a set obtained from a preset database that represents the mapping relationship between the average sampling accuracy evaluation index, the preset input signal extreme value of the analog-to-digital converter, the voltage withstand voltage deviation coefficient and the optimized resistance value.

[0039] Specifically, the voltage withstand voltage deviation coefficient is obtained by performing deviation processing on the obtained voltage and the rated voltage obtained from the preset database. The preset input signal extreme values ​​of the analog-to-digital converter include the preset input signal maximum value and the preset input signal minimum value; the rated voltage is set according to the preset personnel; the preset input signal extreme values ​​of the analog-to-digital converter are set according to the preset personnel, for example, according to the reference voltage of the analog-to-digital converter. If the external reference voltage of the analog-to-digital converter is 5V, the preset input signal extreme values ​​are 0V and 5V respectively.

[0040] In this embodiment, the controllable resistor is a controllable resistor in the oversampling circuit within the sampling sample of the electric vehicle; the main function of the average sampling accuracy evaluation index is to reflect the reliability and error of the data during the sampling process. If the sampling accuracy evaluation index is lower, it is necessary to improve the sampling accuracy by optimizing the resistance value; the preset input signal extreme value of the analog-to-digital converter affects the conversion of the analog signal to the digital signal. If the input signal of the analog-to-digital converter exceeds its quantization range, it may cause distortion or inaccurate data. Therefore, optimizing the resistance value to ensure that the input signal remains within the working range of the analog-to-digital converter can maximize the quality of the sampled data; the voltage withstand deviation coefficient affects the stability of the electric vehicle high-voltage system and the anti-interference ability of the electrical system. The impact of voltage changes on current may cause resistance changes in the circuit.

[0041] The resistance tolerance coefficient may cause gain deviation in the signal conditioning circuit, thereby affecting the input signal amplitude; the resistance tolerance coefficient may cause the reference voltage to be unstable, thereby increasing the voltage standard deviation; the input signal amplitude deviation may be caused by the voltage standard deviation. If the power supply voltage is unstable, the voltage standard deviation will be large, which will cause the input signal amplitude to change; the input signal amplitude deviation may also affect the voltage standard deviation through the feedback mechanism; the smaller the resistance tolerance coefficient, the input signal amplitude deviation coefficient and the voltage standard deviation, the worse the sampling accuracy in the insulation performance pre-test may be due to the resistance difference, and the larger the sampling accuracy evaluation index may be.

[0042] By performing relative deviation processing on the preset electric vehicle sample resistance value and the preset resistance value, the degree of deviation between the actual resistance and the preset resistance is reflected. By performing relative deviation processing on the input signal amplitude of the analog-to-digital converter within the preset electric vehicle sample and the preset input signal amplitude, the deviation between the actual input signal amplitude and the preset value is reflected, and the accuracy of the signal during the conversion process is evaluated. Through these steps, the sampling accuracy of the insulation performance pre-test caused by resistance differences is quantitatively evaluated, providing a basis for subsequent dynamic resistance adjustment, thereby effectively reducing sampling errors caused by circuit component differences, improving sampling accuracy, and ensuring the stability of the electric vehicle insulation performance test. By dynamically adjusting the resistance, the electric vehicle insulation performance test is maintained in the optimal state, thereby improving the accuracy of the insulation performance test. By considering the voltage withstand voltage deviation coefficient and the preset input signal extreme value, the accurate conversion and transmission of the electric vehicle insulation performance test signal is ensured.

[0043] Furthermore, a determination is made as to whether to perform a dynamic insulation fault assessment based on a second insulation resistance error. The specific process is as follows: the second insulation resistance value and a preset insulation resistance value are subjected to deviation processing to obtain a second insulation resistance error; a determination is made as to whether the second insulation resistance error is greater than a preset insulation resistance error obtained from a preset database; if the second insulation resistance error is greater than the preset insulation resistance error obtained from the preset database, a dynamic insulation fault assessment is performed based on dynamic insulation fault parameters during the dynamic resistance adjustment process; the preset insulation resistance error is set by a preset person, for example, by an average value of the insulation resistance error over a historical time period. If the second insulation resistance error is not greater than the preset insulation resistance error obtained from the preset database, it indicates that the second test sampling data of the preset electric vehicle sample is qualified. After performing a second insulation performance pre-test on the preset electric vehicle sample to obtain second test sampling data, a determination is made as to whether to perform test sampling optimization based on the confidence interval of the second test sampling data.

[0044] The dynamic insulation fault assessment is performed based on the dynamic insulation fault parameters during the dynamic resistance adjustment process. The specific method is as follows:

[0045] B1, if the resistance adjustment time is less than the high-voltage transient response time obtained from the preset database, a deviation comparison is performed based on the resistance adjustment time and the high-voltage transient response time obtained from the preset database to obtain a resistance adjustment deviation coefficient; otherwise, the resistance adjustment deviation coefficient is recorded as 0; the specific restriction expression of the resistance adjustment deviation coefficient is:

[0046] ;

[0047] Where RQ represents the resistance adjustment deviation coefficient, and RQT represents the resistance adjustment time, which represents the time required for the resistance value measured by a high-speed oscilloscope to change. Indicates the high-voltage transient response time, which is set according to the preset personnel, for example, according to the high-voltage transient response requirement (such as 100μs).

[0048] B2: If the fault detection delay is less than the preset fault detection delay obtained from the preset database, a deviation comparison is performed based on the fault detection delay and the preset fault detection delay obtained from the preset database to obtain a fault detection delay deviation coefficient. Otherwise, the fault detection delay deviation coefficient is recorded as 0. The specific restriction expression of the fault detection delay deviation coefficient is:

[0049] ;

[0050] Where GY represents the fault detection delay deviation coefficient, GYC represents the fault detection delay, which represents the time required from the occurrence of a fault to its detection recorded in the electric vehicle insulation performance test system; Indicates a preset fault detection delay, which is set according to a preset person, for example, according to the time of simulated fast insulation fault detection (such as 100ns).

[0051] B3, compare the signal distortion rate with the preset signal distortion rate obtained from the preset database to obtain the signal distortion rate comparison coefficient, that is, , where XJB represents the signal distortion rate, which indicates the degree of signal distortion during transmission or processing, and is obtained by comparing the distortion of the measured signal before and after resonance (such as sine wave distortion); Indicates a preset signal distortion rate, which is set by a preset person, for example, represented by the average value of the signal distortion rate in a historical time period.

[0052] B4, compare the conducted noise with the preset conducted noise obtained from the preset database to obtain the conducted noise contrast coefficient, that is, , where CGZ represents the conducted noise, which is represented by the noise on the power line measured by the line impedance stabilization network; Indicates the preset conducted noise, which is set by a preset person, for example, represented by the average value of the conducted noise in a historical time period.

[0053] B5. A dynamic fault compensation factor is introduced to perform assignment and coupling processing on the resistance adjustment deviation coefficient, the fault detection delay deviation coefficient, the denormalized DC voltage standard deviation, the signal distortion rate comparison coefficient, and the conducted noise comparison coefficient to obtain a dynamic insulation fault index. The dynamic insulation fault index is used to quantitatively evaluate the degree of abnormality in the insulation performance test after dynamic resistance adjustment. The dynamic fault compensation factors include a first dynamic fault compensation factor, a second dynamic fault compensation factor, a third dynamic fault compensation factor, a fourth dynamic fault compensation factor, and a fifth dynamic fault compensation factor.

[0054] Among them, the specific limiting expression of the dynamic insulation fault index is:

[0055] ;

[0056] Where DT represents the dynamic insulation fault index, RQ represents the resistance adjustment deviation coefficient, and DYB represents the DC voltage standard deviation during DC voltage injection. represents the first dynamic fault compensation factor, represents the second dynamic fault compensation factor, represents the third dynamic fault compensation factor, represents the fourth dynamic fault compensation factor, represents the fifth dynamic fault compensation factor.

[0057] The above-mentioned dynamic fault compensation factors are obtained from a preset database. The first dynamic fault compensation factor represents the influence of the resistance adjustment time on the dynamic insulation fault index. The second dynamic fault compensation factor represents the influence of the fault detection delay on the dynamic insulation fault index. The third dynamic fault compensation factor represents the influence of the DC voltage standard deviation on the dynamic insulation fault index during the DC voltage injection process. The fourth dynamic fault compensation factor represents the influence of the signal distortion rate on the dynamic insulation fault index. The fifth dynamic fault compensation factor represents the influence of the conducted noise on the dynamic insulation fault index. The sum of the five is 1. For example, the resistance adjustment time and the preset first dynamic fault compensation factor form a resistance adjustment time mapping set. The real-time resistance adjustment time is input into the resistance adjustment time mapping set to obtain the corresponding first dynamic fault compensation factor. The fault detection delay and the preset second dynamic fault compensation factor form a resistance adjustment time mapping set to obtain the corresponding first dynamic fault compensation factor. The DC voltage standard deviation during the DC voltage injection process and the preset third dynamic fault compensation factor form a DC voltage standard deviation mapping set, and the DC voltage standard deviation during the real-time DC voltage injection process is input into the DC voltage standard deviation mapping set to obtain the corresponding third dynamic fault compensation factor; the signal distortion rate and the preset fourth dynamic fault compensation factor form a signal distortion rate mapping set, and the real-time signal distortion rate is input into the signal distortion rate mapping set to obtain the corresponding fourth dynamic fault compensation factor; the conducted noise and the preset fifth dynamic fault compensation factor form a conducted noise mapping set, and the real-time conducted noise is input into the conducted noise mapping set to obtain the corresponding fifth dynamic fault compensation factor; the mapping relationship can be one-to-one or many-to-one.

[0058] In this embodiment, by monitoring the error in the second insulation resistance value, insulation faults can be detected in a timely manner, providing a screening mechanism for subsequent dynamic insulation faults and avoiding unnecessary dynamic evaluations, thereby improving the efficiency of electric vehicle insulation performance testing. In the algorithm of this embodiment, a larger resistance adjustment deviation coefficient may cause a delayed triggering of the fault signal, thereby increasing the fault detection delay; a larger conduction noise may cause signal distortion, thereby increasing the signal distortion rate; a larger signal distortion rate may lead to misjudgment of the fault signal, thereby increasing the fault detection delay; a larger conduction noise may lead to a larger fault detection delay; a larger resistance adjustment deviation coefficient may introduce more noise, resulting in an increased signal distortion rate; the conduction noise will be directly superimposed on the DC voltage signal, resulting in an increase in the DC voltage standard deviation during the DC voltage injection process; a larger DC voltage standard deviation may lead to misjudgment of the fault signal, thereby increasing the fault detection delay; a larger resistance adjustment time, fault detection delay, DC voltage standard deviation, signal distortion rate, and conduction noise may lead to a larger dynamic insulation fault index.

[0059] The timeliness of resistance adjustment is reflected by evaluating the resistance adjustment deviation coefficient; the response delay of electric vehicle insulation fault detection is measured by evaluating the fault detection delay deviation coefficient, thereby avoiding the increase in the degree of insulation fault caused by response delay; the degree of data distortion during transmission or processing is quantified by evaluating the signal distortion rate contrast coefficient; the degree of noise interference in the electric vehicle insulation performance test signal is measured by evaluating the conducted noise contrast coefficient; through the above steps, the degree of abnormality of the insulation performance test after dynamic resistance adjustment is quantitatively evaluated, effectively correcting the abnormality caused by dynamic resistance adjustment, improving the dynamic response capability of the electric vehicle insulation performance test, and improving the stability of the electric vehicle insulation performance test.

[0060] Furthermore, the specific process of optimizing the dynamic insulation performance test based on the results of the dynamic insulation fault assessment is as follows:

[0061] C1, determines whether the dynamic insulation fault index is greater than the preset dynamic insulation fault threshold obtained from the preset database. If the dynamic insulation fault index is not greater than the preset dynamic insulation fault threshold obtained from the preset database, the damping resistance is increased. Otherwise, it directly prompts the preset personnel that the insulation performance test is abnormal; the preset dynamic insulation fault threshold is represented by the average value of the dynamic insulation fault index in the historical time period.

[0062] The damping resistance is increased by adjusting the resistance value of the series resistor in the resonant circuit to the resonant resistance value. The resonant resistance value represents the data obtained by inputting the dynamic insulation fault index and load parameters into the resonant resistance mapping set. The resonant resistance mapping set is a set of mapping relationships between the dynamic insulation fault index, load parameters and resonant resistance values ​​obtained from a preset database. The load parameters include current and voltage. The damping resistor is a resistor used to regulate the circuit in the electrical system, usually used to reduce current fluctuations and stabilize the system. The resonant resistance refers to the resistance value associated with the resonant circuit in the circuit.

[0063] C2, determine whether the dynamic insulation fault index after the damping resistance is increased is greater than the preset dynamic insulation fault threshold obtained from the preset database. If the dynamic insulation fault index after the damping resistance is increased is not greater than the preset dynamic insulation fault threshold obtained from the preset database, the resistance adjustment time is optimized; otherwise, the dynamic insulation performance test optimization is terminated.

[0064] C3, determines whether the dynamic insulation fault index after the resistance adjustment time is optimized is greater than the preset dynamic insulation fault threshold obtained from the preset database. If the dynamic insulation fault index after the resistance adjustment time is optimized is not greater than the preset dynamic insulation fault threshold obtained from the preset database, filtering optimization is performed, otherwise the dynamic insulation performance test optimization is terminated.

[0065] C4, judging whether the dynamic insulation fault index after filtering optimization is greater than the preset dynamic insulation fault threshold obtained from the preset database; if the dynamic insulation fault index after filtering optimization is not greater than the preset dynamic insulation fault threshold obtained from the preset database, the output frequency is adjusted; otherwise, the dynamic insulation performance test optimization is terminated.

[0066] C5, determines whether the dynamic insulation fault index after the output frequency is adjusted is greater than the preset dynamic insulation fault threshold obtained from the preset database. If the dynamic insulation fault index after the output frequency is adjusted is not greater than the preset dynamic insulation fault threshold obtained from the preset database, feedback is given; otherwise, the dynamic insulation performance test optimization is terminated.

[0067] After the dynamic insulation performance test optimization is completed, a third insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain third test sampling data, and then it is determined whether to perform test sampling sample optimization based on the confidence interval of the third test sampling data.

[0068] The dynamic insulation performance test optimization involved includes increasing the damping resistance, optimizing the resistance adjustment time, optimizing filtering, and adjusting the output frequency.

[0069] Output frequency adjustment means adjusting the operating frequency of the electric vehicle insulation performance test to the adjusted output frequency. The adjusted output frequency is obtained by inputting the dynamic insulation fault index and the load parameter into the output frequency mapping set. The output frequency mapping set is a set obtained from a preset database that represents the mapping relationship between the dynamic insulation fault index, the load parameter and the adjusted output frequency.

[0070] Optimizing the resistance adjustment time includes using solid-state relays (SSRs) and pre-switching settings; SSRs can shorten the resistance adjustment time to the 10μs level.

[0071] The pre-switching setting indicates whether to trigger the pre-switching based on the voltage change rate. The pre-switching based on the voltage change rate means that if the voltage change rate is greater than the preset voltage change rate obtained from the preset database, the current circuit will be switched to the preset low-impedance circuit, otherwise the electric vehicle insulation performance test will continue on the current circuit; the preset voltage change rate is set according to the preset personnel, for example, it is expressed by the average value of the voltage change; the preset low-impedance circuit is set according to the preset personnel.

[0072] Filter optimization includes cutoff frequency adjustment and inductor-capacitor adjustment.

[0073] The cutoff frequency adjustment means setting the cutoff frequency of the filter to the optimized cutoff frequency. The optimized cutoff frequency is obtained by inputting the dynamic insulation fault index and the resonant frequency into the cutoff frequency mapping set. The cutoff frequency mapping set is a set obtained from a preset database that represents the mapping relationship between the dynamic insulation fault index, the resonant frequency and the optimized cutoff frequency.

[0074] Inductor-capacitor adjustment is achieved by adjusting the inductance value of the common-mode inductor and the capacitance value of the differential-mode capacitor in the power line and signal line of the electric vehicle. The inductance value of the common-mode inductor is obtained by inputting the transient resonance and load parameters of the inductor into the inductance mapping set, and the capacitance value of the differential-mode capacitor is obtained by inputting the transient resonance and load parameters of the capacitor into the capacitance mapping set. The inductance mapping set is a set of mapping relationships between the transient resonance, load parameters and inductance values ​​of the common-mode inductor obtained from a preset database, and the capacitance mapping set is a set of mapping relationships between the transient resonance, load parameters and capacitance values ​​of the differential-mode capacitor obtained from a preset database; common-mode inductance refers to the inductance used in the circuit to suppress common-mode interference, and common-mode interference refers to interference signals with the same voltage changes on two signal lines; differential-mode capacitance refers to the capacitance used in the circuit to suppress differential-mode interference, and differential-mode interference refers to interference signals with different voltage changes between two signal lines.

[0075] In this embodiment, if Figure 2 As shown, it is a flowchart of dynamic insulation fault assessment, judgment and optimization provided by an embodiment of the present application. After the dynamic insulation fault assessment, it is determined whether the dynamic insulation fault index is greater than the preset dynamic insulation fault threshold. If not, the damping resistance is increased. Otherwise, the preset personnel are directly prompted that the insulation performance test is abnormal. It is determined whether the dynamic insulation fault index after the damping resistance is increased is greater than the preset dynamic insulation fault threshold. If not, the resistance adjustment time is optimized. Otherwise, the insulation performance test is performed. It is determined whether the dynamic insulation fault index after the resistance adjustment time is optimized is greater than the preset dynamic insulation fault threshold. If not, filtering optimization is performed. Otherwise, the insulation performance test is performed. It is determined whether the dynamic insulation fault index after filtering optimization is greater than the preset dynamic insulation fault threshold. If not, the output frequency is adjusted. Otherwise, the insulation performance test is performed. It is determined whether the dynamic insulation fault index after the output frequency adjustment is greater than the preset dynamic insulation fault threshold. If not, feedback is given. Otherwise, the insulation performance test is performed.

[0076] Through real-time monitoring and optimization of the dynamic insulation fault index, the accuracy of electric vehicle insulation performance testing can be effectively improved; through dynamic insulation performance test optimization, the insulation performance of electric vehicles can be optimized in real time, the occurrence rate of insulation faults can be reduced, and good insulation status can be ensured under different working conditions.

[0077] By increasing the damping resistance, the current fluctuation in the high-voltage system of the electric vehicle is reduced, and the stability of the electric vehicle insulation performance test is improved; by optimizing the resistance adjustment time parameters, more precise adjustment of the insulation performance of the electric vehicle is ensured; by optimizing the parameter setting of the filter, noise can be reduced and the accuracy of the acquisition of test sampling data in the electric vehicle insulation performance test can be improved; by increasing the common-mode inductance, common-mode interference can be reduced; by increasing the differential-mode capacitance, differential-mode interference can be suppressed, thereby achieving the accuracy of the electric vehicle insulation performance test and reducing errors caused by signal interference or electrical instability; through the automatic adjustment of parameters through the mapping relationship in the preset database, the efficiency of the electric vehicle insulation performance test is improved.

[0078] Furthermore, the specific process of determining whether to perform test sampling optimization based on the confidence interval of the third test sampling data is as follows:

[0079] Confidence processing is performed on the average value and standard deviation of the insulation resistance values ​​in the third test sampling data to obtain a confidence interval.

[0080] Determine whether the confidence interval width is greater than the preset confidence interval width obtained from the preset database. If the confidence interval width is greater than the preset confidence interval width obtained from the preset database, generate sampling samples, otherwise do not perform test sampling sample optimization; the preset confidence interval width is set according to the preset personnel.

[0081] Determine whether the confidence interval width after the sampling sample is generated is greater than the preset confidence interval width obtained from the preset database. If the confidence interval width after the sampling sample is generated is greater than the preset confidence interval width obtained from the preset database, adjust the sampling frequency, otherwise end the test sampling sample optimization.

[0082] Determine whether the confidence interval width after the sampling frequency is adjusted is greater than the preset confidence interval width obtained from the preset database. If the confidence interval width after the sampling frequency is adjusted is greater than the preset confidence interval width obtained from the preset database, feedback is given; otherwise, the test sampling sample optimization is terminated.

[0083] The test sampling sample optimization involved includes sampling sample generation and sampling frequency adjustment; sampling sample generation means using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm to generate the load parameters and test sampling data of the adjusted sampling samples based on the load parameters of the preset sampling samples and the third test sampling data; the SMOTE algorithm generates new minority class samples by interpolating between minority class samples, thereby balancing the sampling samples. For each minority class sample, its k nearest neighbors are found (usually k=5), and then a sample is randomly selected from the k nearest neighbors. Based on the difference in the eigenvectors between the two samples, a new minority class sample is generated according to a certain ratio (usually a random number less than 1).

[0084] Sampling frequency adjustment means adjusting the current test sampling frequency to the optimized test sampling frequency. The optimized test sampling frequency is obtained by inputting the sampling accuracy evaluation index, the number of test samples, and the insulation resistance error after optimization of the dynamic insulation performance test into the sampling frequency mapping set. The sampling frequency mapping set is a set obtained from a preset database that represents the mapping relationship between the sampling accuracy evaluation index, the number of test samples, the insulation resistance error after optimization of the dynamic insulation performance test, and the optimized test sampling frequency.

[0085] In this embodiment, by judging the width of the confidence interval, problems in the test sampling data, such as insufficient samples and large data fluctuations, can be discovered in a timely manner. The narrower the confidence interval, the more accurate the test sampling data. By generating sampling samples and adjusting the sampling frequency, the diversity and richness of the samples can be increased, and the impact of random errors can be reduced, thereby improving the comprehensiveness of the test sampling data in the electric vehicle insulation performance test, avoiding decision-making errors in the dynamic adjustment resistance and dynamic insulation performance test optimization due to incomplete test sampling data, and thus achieving the accuracy of the electric vehicle insulation performance test.

[0086] New sampling samples are generated through the SMOTE algorithm to increase the diversity of sampling samples, so that the sampling samples better cover the feature space of the data, thereby improving the comprehensiveness of the test sampling data in the electric vehicle insulation performance test; by optimizing the sampling frequency, the accuracy and completeness of the test sampling data are improved, thereby achieving the accuracy of the electric vehicle insulation performance test.

[0087] like Figure 3As shown, a structural diagram of an electric vehicle insulation performance testing system based on big data provided by an embodiment of the present application is provided. The system of an electric vehicle insulation performance testing method based on big data provided by an embodiment of the present application includes: a test sampling accuracy evaluation module, a first insulation resistance error judgment module, a second insulation resistance error judgment module and a dynamic insulation fault evaluation judgment module; wherein the test sampling accuracy evaluation module is used to perform a first insulation performance pre-test on a preset electric vehicle sampling sample during a pre-sampling period to obtain a first test sampling data, and at the same time perform a test sampling accuracy evaluation based on the test sampling accuracy evaluation parameters of the first insulation performance pre-test process to determine whether to dynamically adjust the resistance; the first insulation resistance error judgment module is used to, if dynamic resistance adjustment is not performed, obtain a first insulation resistance value by injecting a preset DC voltage into the high-voltage system of the electric vehicle, and perform deviation processing on the first insulation resistance value and the preset insulation resistance value to obtain a first insulation resistance error, determine whether to perform feedback based on the first insulation resistance error, and determine whether to perform test sampling sample optimization based on the confidence interval of the first test sampling data, if the test sampling sample optimization is not performed, perform insulation performance test on the preset electric vehicle sampling sample, otherwise perform insulation performance test on the preset electric vehicle sampling sample after the test sampling sample optimization The electric vehicle sampling sample is subjected to an insulation performance test; the second insulation resistance value error judgment module is used for, if dynamic resistance adjustment is performed, injecting a preset DC voltage into the high-voltage system of the electric vehicle after the dynamic resistance adjustment to obtain a second insulation resistance value, and performing deviation processing on the second insulation resistance value and the preset insulation resistance value to obtain a second insulation resistance value error, and judging whether to perform dynamic insulation fault assessment based on the second insulation resistance value error; if dynamic insulation fault assessment is not performed, performing a second insulation performance pre-test on the preset electric vehicle sampling sample to obtain second test sampling data, and then judging whether to perform test sampling sample optimization based on the confidence interval of the second test sampling data; otherwise, executing the function of the dynamic insulation fault assessment judgment module; the dynamic insulation fault assessment judgment module is used for, if dynamic insulation fault assessment is performed, performing a dynamic insulation performance test optimization based on the result of the dynamic insulation fault assessment, performing a third insulation performance pre-test on the preset electric vehicle sampling sample to obtain third test sampling data, and then judging whether to perform test sampling sample optimization based on the confidence interval of the third test sampling data; if test sampling sample optimization is not performed, performing an insulation performance test on the preset electric vehicle sampling sample; otherwise, performing an insulation performance test on the preset electric vehicle sampling sample after the test sampling sample optimization.

[0088] In this embodiment, Figure 3It can be seen that after executing the function of the test sampling accurate evaluation module, the function of the first insulation resistance value error judgment module may be executed, and the function of the second insulation resistance value error judgment module may also be executed. After executing the function of the test sampling accurate evaluation module, the function of the dynamic insulation fault evaluation and judgment module may not be executed.

[0089] Through the test sampling accuracy evaluation module, errors and problems that may exist in the test sampling process can be discovered in advance, and it can be judged in time whether dynamic resistance adjustment is needed, thus laying the foundation for the subsequent accurate measurement of the insulation resistance value, and effectively improving the accuracy of the test sampling; through the first insulation resistance value error judgment module and the second insulation resistance value error judgment module, abnormal conditions in the measurement process can be discovered and optimized in time, thereby solving the problem of low accuracy caused by dynamic resistance adjustment in the electric vehicle insulation performance test; through sample optimization, interference factors in the test process are reduced, and the reliability of the electric vehicle insulation performance test is improved, thereby achieving an improvement in the accuracy of the electric vehicle insulation performance test.

[0090] To sum up, the embodiment of the present application determines whether to dynamically adjust the resistance based on the results of accurate evaluation of test sampling, and then determines whether to perform dynamic insulation fault evaluation based on the insulation resistance value error after dynamically adjusting the resistance, and performs dynamic insulation performance test optimization based on the results of dynamic insulation fault evaluation. Finally, based on the confidence interval of the test sampling data, it determines whether to perform test sampling sample optimization, thereby reducing the error of the test sampling data, and further achieving improved accuracy in the insulation performance test of electric vehicles, effectively solving the problem of low accuracy in the insulation performance test of electric vehicles caused by sampling data errors in the prior art.

[0091] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0095] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0096] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for testing the insulation performance of electric vehicles based on big data, characterized in that: The following steps are involved: S1, performing a first insulation performance pre-test on a preset electric vehicle sampling sample during a pre-sampling period to obtain first test sampling data, and simultaneously performing a test sampling accuracy assessment based on a test sampling accuracy assessment parameter of the first insulation performance pre-test process to determine whether to dynamically adjust the resistance; S2: If the resistance is not dynamically adjusted, a first insulation resistance error is obtained by injecting a preset DC voltage into the high-voltage system of the electric vehicle to obtain a first insulation resistance value, and a determination is made based on a confidence interval of the first test sampling data whether to perform test sampling optimization. If the test sampling optimization is not performed, an insulation performance test is performed on the preset electric vehicle sampling sample; otherwise, an insulation performance test is performed after the test sampling optimization. S3, if the resistance is dynamically adjusted, a second insulation resistance value is obtained by injecting a preset DC voltage into the high-voltage system of the electric vehicle after the dynamic resistance adjustment to obtain a second insulation resistance value to obtain a second insulation resistance value error, and based on the second insulation resistance value error, it is determined whether to perform a dynamic insulation fault assessment; if not, a second insulation performance pre-test is performed on a preset electric vehicle sampling sample to obtain second test sampling data, and based on the confidence interval of the second test sampling data, it is determined whether to perform test sampling sample optimization; otherwise, S4 is executed; S4, if a dynamic insulation fault assessment is performed, then after optimizing the dynamic insulation performance test according to the results of the dynamic insulation fault assessment, a third insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain third test sampling data, and then, based on the confidence interval of the third test sampling data, it is determined whether to optimize the test sampling sample; if not, an insulation performance test is performed on the preset electric vehicle sampling sample; otherwise, an insulation performance test is performed after the test sampling sample is optimized.

2. The electric vehicle insulation performance testing method based on big data according to claim 1, characterized in that: The test sampling accuracy evaluation is performed according to the test sampling accuracy evaluation parameters of the first insulation performance pre-test process, and the specific method is as follows: A resistance tolerance coefficient is obtained by performing relative deviation processing on the insulation resistance value of the preset electric vehicle sampling sample and the preset resistance value obtained from the preset database; An input signal amplitude deviation coefficient is obtained by performing relative deviation processing on the input signal amplitude of the analog-to-digital converter in the preset electric vehicle sampling sample and the preset input signal amplitude obtained from the preset database; A sampling accuracy assessment compensation factor is introduced to assign and couple the resistance tolerance coefficient, the input signal amplitude deviation coefficient, and the de-normalized voltage standard deviation. An inverse proportional operation is performed to obtain a sampling accuracy assessment index, which is used to quantitatively assess the sampling accuracy in the insulation performance pre-test caused by resistance differences.

3. The electric vehicle insulation performance testing method based on big data as claimed in claim 2, characterized in that: The specific process of determining whether to dynamically adjust the resistance is as follows: Determine whether the average sampling accuracy evaluation index is greater than the preset sampling accuracy evaluation threshold obtained from the preset database: The average sampling accuracy evaluation index represents the average sampling accuracy of the insulation performance pre-test; If the average sampling accuracy evaluation index is greater than a preset sampling accuracy evaluation threshold obtained from a preset database, dynamically adjusting the resistance; If the average sampling accuracy evaluation index is not greater than the preset sampling accuracy evaluation threshold obtained from the preset database, the electric vehicle insulation performance test is continued; The dynamically adjusted resistance represents adjusting the resistance value of a controllable resistor in a preset high-voltage system of a sampled electric vehicle to an optimized resistance value, wherein the optimized resistance value is obtained by inputting an average sampling accuracy evaluation index, a preset input signal extreme value of an analog-to-digital converter, and a voltage withstand voltage deviation coefficient into an adjusted resistance value mapping set, wherein the adjusted resistance value mapping set is a set obtained from a preset database and represents a mapping relationship between the average sampling accuracy evaluation index, the preset input signal extreme value of the analog-to-digital converter, the voltage withstand voltage deviation coefficient, and the optimized resistance value; The voltage withstand voltage deviation coefficient is obtained by performing deviation processing on the obtained voltage and the rated voltage obtained from the preset database. The preset input signal extreme values ​​of the analog-to-digital converter include a preset input signal maximum value and a preset input signal minimum value.

4. The electric vehicle insulation performance testing method based on big data according to claim 1, characterized in that: The specific process of determining whether to perform dynamic insulation fault assessment based on the second insulation resistance error is as follows: Performing deviation processing on the second insulation resistance value and the preset insulation resistance value to obtain a second insulation resistance value error; Determine whether the second insulation resistance value error is greater than the preset insulation resistance value error obtained from the preset database: If the second insulation resistance value error is greater than the preset insulation resistance value error obtained from the preset database, a dynamic insulation fault assessment is performed according to the dynamic insulation fault parameter in the process of dynamically adjusting the resistance; If the second insulation resistance error is not greater than the preset insulation resistance error obtained from the preset database, a second insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain second test sampling data, and then a determination is made based on the confidence interval of the second test sampling data whether to perform test sampling optimization.

5. The electric vehicle insulation performance testing method based on big data as claimed in claim 4, characterized in that: The dynamic insulation fault assessment is performed according to the dynamic insulation fault parameters during the dynamic resistance adjustment process. The specific method is as follows: If the resistance adjustment time is less than the high-voltage transient response time obtained from the preset database, a resistance adjustment deviation coefficient is obtained by performing a deviation comparison process based on the resistance adjustment time and the high-voltage transient response time obtained from the preset database; otherwise, the resistance adjustment deviation coefficient is recorded as 0; If the fault detection delay is less than the preset fault detection delay obtained from the preset database, a deviation comparison process is performed based on the fault detection delay and the preset fault detection delay obtained from the preset database to obtain a fault detection delay deviation coefficient; otherwise, the fault detection delay deviation coefficient is recorded as 0; Comparing the signal distortion rate with a preset signal distortion rate obtained from a preset database to obtain a signal distortion rate comparison coefficient; Comparing the conducted noise with a preset conducted noise obtained from a preset database to obtain a conducted noise contrast coefficient; A dynamic fault compensation factor is introduced to perform assignment and coupling processing on the resistance adjustment deviation coefficient, fault detection delay deviation coefficient, denormalized DC voltage standard deviation, signal distortion rate comparison coefficient, and conducted noise comparison coefficient to obtain a dynamic insulation fault index. The dynamic insulation fault index is used to quantitatively evaluate the degree of abnormality in the insulation performance test after dynamic resistance adjustment.

6. The electric vehicle insulation performance testing method based on big data according to claim 5, characterized in that: The specific process of optimizing the dynamic insulation performance test based on the results of the dynamic insulation fault assessment is as follows: If the dynamic insulation fault index is not greater than the preset dynamic insulation fault threshold obtained from the preset database, the damping resistance is increased, otherwise the preset personnel is directly prompted that the insulation performance test is abnormal; The damping resistance is increased by adjusting the resistance of a resistor connected in series in the resonant circuit to a resonant resistance value, wherein the resonant resistance value represents data obtained by inputting a dynamic insulation fault index and a load parameter into a resonant resistance mapping set, wherein the resonant resistance mapping set is a set obtained from a preset database and represents a mapping relationship between the dynamic insulation fault index, the load parameter, and the resonant resistance value, wherein the load parameter includes current and voltage; If the dynamic insulation fault index after the damping resistance is increased is not greater than the preset dynamic insulation fault threshold obtained from the preset database, the resistance adjustment time is optimized, otherwise the dynamic insulation performance test optimization is terminated; If the dynamic insulation fault index after the resistance adjustment time is optimized is not greater than the preset dynamic insulation fault threshold obtained from the preset database, filtering optimization is performed, otherwise the dynamic insulation performance test optimization is terminated; If the dynamic insulation fault index after filtering optimization is not greater than the preset dynamic insulation fault threshold obtained from the preset database, the output frequency is adjusted, otherwise the dynamic insulation performance test optimization is terminated; If the dynamic insulation fault index after the output frequency adjustment is not greater than the preset dynamic insulation fault threshold obtained from the preset database, feedback is given; otherwise, the dynamic insulation performance test optimization is terminated; The output frequency adjustment means adjusting the operating frequency of the electric vehicle insulation performance test to an adjusted output frequency, wherein the adjusted output frequency is obtained by inputting a dynamic insulation fault index and a load parameter into an output frequency mapping set, and the output frequency mapping set is a set obtained from a preset database and represents a mapping relationship between the dynamic insulation fault index, the load parameter and the adjusted output frequency.

7. The electric vehicle insulation performance testing method based on big data according to claim 6, characterized in that: The dynamic insulation performance test optimization includes increasing the damping resistance, optimizing the resistance adjustment time, optimizing the filter and adjusting the output frequency; The resistance adjustment time optimization includes using solid-state relays and pre-switching settings; The pre-switching setting indicates whether to trigger the pre-switching based on the voltage change rate. The pre-switching setting indicates that if the voltage change rate is greater than a preset voltage change rate obtained from a preset database, the current circuit is switched to a preset low-impedance circuit, otherwise the electric vehicle insulation performance test is continued on the current circuit. The filtering optimization includes cutoff frequency adjustment and inductance-capacitance adjustment; The cutoff frequency adjustment means setting the cutoff frequency of the filter to an optimized cutoff frequency, wherein the optimized cutoff frequency is obtained by inputting a dynamic insulation fault index and a resonant frequency into a cutoff frequency mapping set, wherein the cutoff frequency mapping set is a set obtained from a preset database and represents a mapping relationship between the dynamic insulation fault index, the resonant frequency, and the optimized cutoff frequency; The inductor-capacitor adjustment is performed by adjusting the inductance value of the common-mode inductor and the capacitance value of the differential-mode capacitor in the power line and signal line of the electric vehicle. The inductance value of the common-mode inductor is obtained by inputting the transient resonance and load parameters of the inductor into the inductor mapping set, and the capacitance value of the differential-mode capacitor is obtained by inputting the transient resonance and load parameters of the capacitor into the capacitance mapping set. The inductor mapping set is a set of mapping relationships between the transient resonance of the inductor, the load parameters, and the inductance value of the common-mode inductor obtained from a preset database. The capacitance mapping set is a set of mapping relationships between the transient resonance of the capacitor, the load parameters, and the capacitance value of the differential-mode capacitor obtained from a preset database.

8. The electric vehicle insulation performance testing method based on big data according to claim 1, characterized in that: The specific process of determining whether to perform test sampling optimization based on the confidence interval of the third test sampling data is as follows: Perform confidence processing based on the average value and standard deviation of the third insulation resistance value to obtain a confidence interval; Determine whether the confidence interval width is greater than the preset confidence interval width obtained from the preset database. If the confidence interval width is greater than the preset confidence interval width obtained from the preset database, generate a sampling sample; otherwise, do not optimize the test sampling sample. If the confidence interval width after the sampling sample is generated is greater than the preset confidence interval width obtained from the preset database, the sampling frequency is adjusted, otherwise the test sampling sample optimization is terminated; If the confidence interval width after the sampling frequency adjustment is greater than the preset confidence interval width obtained from the preset database, feedback is given; otherwise, the test sampling sample optimization is terminated.

9. The electric vehicle insulation performance testing method based on big data according to claim 8, characterized in that: The test sampling sample optimization includes sampling sample generation and sampling frequency adjustment; The sampling sample generation means generating the load parameter of the adjusted sampling sample and the test sampling data according to the load parameter of the preset sampling sample and the third test sampling data by using the SMOTE algorithm; The sampling frequency adjustment means adjusting the current test sampling frequency to the optimized test sampling frequency, and the optimized test sampling frequency is obtained by inputting the sampling accuracy evaluation index, the test sampling number, and the insulation resistance error after the dynamic insulation performance test optimization into the sampling frequency mapping set. The sampling frequency mapping set is a set obtained from a preset database that represents the mapping relationship between the sampling accuracy evaluation index, the test sampling number, the insulation resistance error after the dynamic insulation performance test optimization, and the optimized test sampling frequency.

10. A system using the electric vehicle insulation performance testing method based on big data according to any one of claims 1 to 9, characterized in that: include: Test sampling accuracy evaluation module, first insulation resistance value error judgment module, second insulation resistance value error judgment module and dynamic insulation fault evaluation and judgment module; The test sampling accuracy evaluation module is used to perform a first insulation performance pre-test on a preset electric vehicle sampling sample during a pre-sampling period to obtain first test sampling data, and at the same time perform a test sampling accuracy evaluation based on the test sampling accuracy evaluation parameters of the first insulation performance pre-test process to determine whether to dynamically adjust the resistance; The first insulation resistance error judgment module is used to obtain a first insulation resistance error by injecting a preset DC voltage into the high-voltage system of the electric vehicle to obtain a first insulation resistance value if the resistance is not dynamically adjusted, and to determine whether to perform test sampling sample optimization based on the confidence interval of the first test sampling data; if the test sampling sample optimization is not performed, then perform an insulation performance test on the preset electric vehicle sampling sample; otherwise, perform an insulation performance test after the test sampling sample optimization; The second insulation resistance value error judgment module is used to obtain a second insulation resistance value error by injecting a preset DC voltage into the high-voltage system of the electric vehicle after the dynamic resistance adjustment if the resistance is dynamically adjusted, and to determine whether to perform dynamic insulation fault assessment based on the second insulation resistance value error. If not, a second insulation performance pre-test is performed on a preset electric vehicle sampling sample to obtain second test sampling data, and then, based on the confidence interval of the second test sampling data, determine whether to perform test sampling sample optimization; otherwise, the function of the dynamic insulation fault assessment judgment module is executed; The dynamic insulation fault assessment and judgment module is used to, if a dynamic insulation fault assessment is performed, perform a dynamic insulation performance test optimization based on the results of the dynamic insulation fault assessment, perform a third insulation performance pre-test on a preset electric vehicle sampling sample to obtain third test sampling data, and then determine whether to perform test sampling sample optimization based on the confidence interval of the third test sampling data. If not, perform an insulation performance test on the preset electric vehicle sampling sample; otherwise, perform an insulation performance test after the test sampling sample optimization.

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