Electric vehicle insulation performance test method and system based on big data

Abstract

The invention discloses an electric vehicle insulation performance testing method and system based on big data, and relates to the technical field of electric vehicle insulation performance testing. The electric vehicle insulation performance test method based on big data comprises the following steps of accurate evaluation of test sampling, first insulation resistance error judgment, second insulation resistance error judgment and dynamic insulation fault evaluation judgment. According to the method, whether the resistance is dynamically adjusted or not is judged according to an accurate evaluation result of test sampling, then whether dynamic insulation fault evaluation is carried out or not is judged based on an insulation resistance error after the resistance is dynamically adjusted, and dynamic insulation performance test optimization is carried out according to a dynamic insulation fault evaluation result. Finally, whether test sampling sample optimization is carried out or not is judged based on the confidence interval of the test sampling data, the accuracy of the insulation performance test of the electric vehicle is improved, and the problem that the accuracy of the insulation performance test of the electric vehicle is low due to sampling data errors in the prior art is solved.

Description

Technical Field

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

[0002] With the increasing global attention to environmental protection and energy efficiency, electric vehicles, as an important alternative to traditional fuel vehicles, have been widely used and developed rapidly. The electrical systems of electric vehicles, including components such as battery packs, electric motors, and inverters, also face relatively high safety risks due to their high voltage and high energy density characteristics. In the electrical system of an electric vehicle, insulation performance is one of the important factors to ensure the safe operation of the vehicle. A decrease in insulation performance may lead to serious safety accidents such as electrical failures, battery short circuits, and even fires. Therefore, the testing and monitoring of the insulation performance of electric vehicles become particularly important.

[0003] In the prior art, through real-time data collection and analysis, various components of electric vehicles can be monitored in a refined manner, and intelligent prediction and diagnosis can be carried out based on historical data and real-time data to timely detect potential faults and insulation performance problems, thereby improving the detection efficiency and accuracy of insulation performance.

[0004] For example, a method for measuring the insulation performance of an electric vehicle disclosed in a patent application with the publication number CN115754476A includes: a resistance mechanism, including resistors R2, R1, and Rs, where resistors R2, R1, and Rs are resistors with dynamically controllable resistance values. Connect resistor R1 to the positive pole of the high-voltage direct current output, and connect resistor R2 to the negative pole of the high-voltage direct current output; the oversampling processing circuit includes a filter conditioning circuit and a microprocessor with an analog-to-digital converter built in. The output end of 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 by cooperating with the adopted dynamically controllable resistors, and the resistance values of the resistors in the resistance network are dynamically controlled and adjusted.

[0005] For example, an electric vehicle insulation detection method announced in the invention patent announcement with the announcement number of CN113009227B includes: Step S10, the charging pile obtains the vehicle insulation resistance protection range of the electric vehicle; Step S20, measure the initial pile insulation value through the pile insulation detector, and calculate the upper and lower limit loading resistance values based on the initial pile insulation value and the vehicle insulation resistance protection range; Step S30, load the upper and lower limit loading resistance values on the charging circuit to verify the vehicle insulation resistance protection range; Step S40, alternately perform insulation detection through the pile and the vehicle insulation detector to obtain the pile insulation value and the vehicle insulation value; Step S50, obtain the insulation error value based on the pile insulation value and the vehicle insulation value, and respectively draw corresponding curves based on the insulation error value, the pile insulation value and the vehicle insulation value; Step S60, detect the insulation performance based on the curves.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it is found that the above technology has at least the following technical problems: In the prior art, due to differences in aspects such as the service life of resistance elements, the resolution or accuracy of analog-to-digital converters, and the changes in loads (such as load changes during battery charging and discharging, motor starting and running, etc.), there may be deviations between the actual resistance value and the standard value of the resistance, the subtle changes in input signals cannot be accurately captured, and the fluctuations of current and voltage in the circuit, resulting in the measured insulation resistance value may cause inaccurate test results and cannot truly reflect the insulation state of the high-voltage system of the electric vehicle; Therefore, after adjusting the resistance in the high-voltage system of the electric vehicle, it may lead to a lag in the adjustable resistance switching time behind the high-voltage transient change and the formation of a resonant circuit between the distributed capacitance and the dynamic resistance network, resulting in signal distortion of the measurement, and may also introduce electromagnetic interference, leading to missed detection of fast insulation faults, and there is a problem of low accuracy in the insulation performance test of electric vehicles due to sampling data errors. Summary of the Invention

[0007] The embodiments of the present application provide a method and system for testing the insulation performance of electric vehicles based on big data, which solve the problem of low accuracy in the insulation performance test of electric vehicles due to sampling data errors in the prior art, and realize the improvement of the accuracy of the insulation performance test of electric vehicles.

[0008] An embodiment of the present application provides a method for testing the insulation performance of an electric vehicle based on big data, including the following steps: S1, perform a first pre-test of the insulation performance on a preset electric vehicle sampling sample during a pre-sampling period to obtain first test sampling data, and at the same time, accurately evaluate the parameters of the test sampling during the first pre-test of the insulation performance to accurately evaluate the test sampling, and determine whether to dynamically adjust the resistance; S2, if the resistance is not dynamically adjusted, obtain the first insulation resistance value error from the first insulation resistance value obtained by injecting a preset DC voltage into the high-voltage system of the electric vehicle, and determine whether to optimize the test sampling sample based on the confidence interval of the first test sampling data. If the test sampling sample is not optimized, perform an insulation performance test on the preset electric vehicle sampling sample, otherwise perform an insulation performance test after optimizing the test sampling sample; S3, if the resistance is dynamically adjusted, obtain the second insulation resistance value error from the second insulation resistance value obtained by injecting a preset DC voltage into the high-voltage system of the electric vehicle after dynamically adjusting the resistance, and determine whether to perform a dynamic insulation fault assessment based on the second insulation resistance value error. If not, perform a second pre-test of the insulation performance on the preset electric vehicle sampling sample to obtain second test sampling data, and then determine whether to optimize the test sampling sample based on the confidence interval of the second test sampling data, otherwise execute S4; S4, if a dynamic insulation fault assessment is performed, perform a dynamic insulation performance test optimization according to the result of the dynamic insulation fault assessment, and then perform a third pre-test of the insulation performance on the preset electric vehicle sampling sample to obtain third test sampling data, and then determine whether to optimize the test sampling sample 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 optimizing the test sampling sample.

[0009] An embodiment of the present application provides a system for an electric vehicle insulation performance testing method based on big data, including: 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 first test sampling data, and at the same time perform a test sampling accuracy evaluation according to the test sampling accuracy evaluation parameters in 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, if the resistance is not dynamically adjusted, obtain a first insulation resistance value error from the first insulation resistance value obtained by injecting a preset DC voltage into the electric vehicle high-voltage system, and judge whether to optimize the test sampling sample based on the confidence interval of the first test sampling data. If the test sampling sample is not optimized, perform an insulation performance test on the preset electric vehicle sampling sample, otherwise perform an insulation performance test after optimizing the test sampling sample; the second insulation resistance value error judgment module is used to, if the resistance is dynamically adjusted, obtain a second insulation resistance value error from the second insulation resistance value obtained by injecting a preset DC voltage into the electric vehicle high-voltage system after dynamically adjusting the resistance, and judge whether to perform a dynamic insulation fault evaluation based on the second insulation resistance value error. If not, perform a second insulation performance pre-test on the preset electric vehicle sampling sample to obtain second test sampling data, and then judge whether to optimize the test sampling sample based on the confidence interval of the second test sampling data, otherwise execute the function of the dynamic insulation fault evaluation judgment module; the dynamic insulation fault evaluation judgment module is used to, if a dynamic insulation fault evaluation is performed, perform a dynamic insulation performance test optimization according to the result of the dynamic insulation fault evaluation, and then perform a third insulation performance pre-test on the preset electric vehicle sampling sample to obtain third test sampling data, and then judge whether to optimize the test sampling sample 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 optimizing the test sampling sample.

[0010] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Determine whether to dynamically adjust the resistance based on the result of the test sampling accuracy evaluation, then judge whether to perform a dynamic insulation fault evaluation based on the insulation resistance value error after dynamically adjusting the resistance, and perform a dynamic insulation performance test optimization according to the result of the dynamic insulation fault evaluation. Finally, judge whether to optimize the test sampling sample based on the confidence interval of the test sampling data, thereby reducing the error of the test sampling data, and further improving the accuracy of the electric vehicle insulation performance test, effectively solving the problem of low accuracy of the electric vehicle insulation performance test caused by sampling data error in the prior art.

[0011] 2. By adjusting the resistance value of the resistor in series in the resonant circuit to the resonant resistance value, then optimizing the resistance adjustment duration, next setting the cut-off frequency of the filter to the optimized cut-off frequency, and at the same time 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, and finally adjusting the operating frequency of the electric vehicle insulation performance test to the adjusted output frequency, the good insulation state of the electric vehicle under different operating conditions is ensured, and further the accuracy of the electric vehicle insulation performance test is improved.

[0012] 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, then input the average sampling accuracy evaluation index, the preset input signal extreme value of the analog-to-digital converter, and the voltage withstand deviation coefficient into the adjusted resistance value mapping set to obtain the optimized resistance value, and adjust the resistance value of the controllable resistor in the high-voltage system of the preset electric vehicle sampling sample to the optimized resistance value, thus ensuring that the electric vehicle insulation performance test remains in the optimal state, and further the accuracy of the insulation performance test is improved. Description of the Drawings

[0013] Figure 1 It is a flowchart of a method for testing the insulation performance of an electric vehicle based on big data provided by an embodiment of the present application; Figure 2 It is a flowchart of dynamic insulation fault evaluation, judgment and optimization provided by an embodiment of the present application; Figure 3 It is a schematic structural diagram of a system for testing the insulation performance of an electric vehicle based on big data provided by an embodiment of the present application. Detailed Embodiments

[0014] In the embodiments of the present application, by providing a method and system for testing the insulation performance of an electric vehicle based on big data, the problem of low accuracy of the electric vehicle insulation performance test caused by sampling data errors in the prior art is solved. The test sampling accuracy is evaluated through the test sampling accuracy evaluation parameters in the first insulation performance pre-test process, and it is judged whether to dynamically adjust the resistor. Then, after dynamically adjusting the resistor, the second insulation resistance value error is obtained according to the second insulation resistance value and the preset insulation resistance value, and it is judged whether to perform dynamic insulation fault evaluation. Finally, after optimizing the dynamic insulation performance test based on the result of the dynamic insulation fault evaluation, it is judged 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, realizing the improvement of the accuracy of the electric vehicle insulation performance test.

[0015] The technical solutions in the embodiments of the present application for solving the problem of low accuracy of the electric vehicle insulation performance test caused by sampling data errors are generally as follows: Based on the results accurately evaluated by test sampling, determine whether to dynamically adjust the resistance. Then, after dynamically adjusting the resistance, determine whether to conduct a dynamic insulation fault assessment based on the insulation resistance value error, and optimize the dynamic insulation performance test according to the results of the dynamic insulation fault assessment. Finally, determine whether to optimize the test sampling samples based on the confidence interval of the test sampling data, improving the accuracy of the electric vehicle insulation performance test.

[0016] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0017] The embodiment of the present application provides a method for testing the insulation performance of an electric vehicle based on big data, including the following steps: S1, accurate evaluation of test sampling: conduct 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, conduct an accurate evaluation of test sampling according to the test sampling accurate evaluation parameters during the process of the first insulation performance pre-test 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 the initial sample set by the preset person.

[0018] S2, judgment of the first insulation resistance value error: if the resistance is not dynamically adjusted, inject a preset DC voltage into the high-voltage system of the electric vehicle to obtain the first insulation resistance value, and perform deviation processing on the first insulation resistance value and the preset insulation resistance value to obtain the first insulation resistance value error. Based on the first insulation resistance value error, determine whether to give feedback, and based on the confidence interval of the first test sampling data, determine whether to optimize the test sampling samples. If the test sampling samples are not optimized, conduct an insulation performance test on the preset electric vehicle sampling sample, otherwise conduct an insulation performance test on the preset electric vehicle sampling sample after optimizing the test sampling samples; the preset DC voltage is set according to the GB / T 18384 standard, and it is required that the injected DC voltage is twice the maximum working DC voltage of the high-voltage system of the electric vehicle; the specific formula for the insulation resistance value error is , where JWC represents the insulation resistance value error, JYZ represents the insulation resistance value, represents the preset insulation resistance value; input the first insulation resistance value into the specific formula for the insulation resistance value error to obtain the first insulation resistance value error; input the average value and standard deviation of the insulation resistance value in the first test sampling data into the calculation formula for the confidence interval to obtain the confidence interval; the specific calculation formula for the confidence interval is , where, is the average value of the insulation resistance value, is the standard deviation of the insulation resistance value, n is the number of samples of the preset electric vehicle sampling sample in the first test sampling data, Z value corresponding to the confidence level under the normal distribution. For a 95% confidence level, the corresponding Z value is 1.96.

[0019] S3. Second insulation resistance value error judgment: If dynamic resistance adjustment is performed, 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 the second insulation resistance value, and the deviation between the second insulation resistance value and the preset insulation resistance value is processed to obtain the 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, after the second insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain the second test sampling data, it is judged whether to optimize the test sampling sample based on the confidence interval of the second test sampling data. Otherwise, S4 is executed; the second insulation resistance value is input into the specific formula of the insulation resistance value error to obtain the second insulation resistance value error; according to the average value and standard deviation of the insulation resistance values in the second test sampling data, they are input into the confidence interval calculation formula to obtain the corresponding confidence interval.

[0020] S4. Dynamic insulation fault assessment judgment: If dynamic insulation fault assessment is performed, after optimizing the dynamic insulation performance test according to the result of the dynamic insulation fault assessment, after the third insulation performance pre-test is performed on the preset electric vehicle sampling sample to obtain the third test sampling data, it is judged whether to optimize the test sampling sample based on the confidence interval of the third test sampling data. If the test sampling sample is not optimized, the insulation performance test is performed on the preset electric vehicle sampling sample. Otherwise, the insulation performance test is performed on the preset electric vehicle sampling sample after the test sampling sample is optimized; 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 of which include but are not limited to the insulation resistance value, voltage, and current.

[0021] In this embodiment, as Figure 1As shown in the figure, it is a flowchart of a method for testing the insulation performance of an electric vehicle based on big data provided by an embodiment of the present application. According to the test sampling accurate evaluation parameters in the process of the first insulation performance pre-test, test sampling accurate evaluation is carried out, and it is judged whether to dynamically adjust the resistance. If the resistance is not dynamically adjusted, it is judged whether to optimize the test sampling sample based on the confidence interval of the first test sampling data, and the insulation performance test is carried out; if the resistance is dynamically adjusted, it is judged whether to carry out dynamic insulation fault evaluation based on the second insulation resistance value error. If the dynamic insulation fault evaluation is not carried out, after the second insulation performance pre-test is carried out on the preset electric vehicle sampling sample to obtain the second test sampling data, it is judged whether to optimize the test sampling sample based on the confidence interval of the second test sampling data, otherwise it is judged whether to optimize the test sampling sample based on the confidence interval of the third test sampling data, and the insulation performance test is carried out.

[0022] Due to differences in aspects such as the service life of the resistance element, there may be a deviation between the actual resistance value and the standard value of the resistance. Due to the insufficient resolution or accuracy of the analog-to-digital converter, subtle changes in the input signal may not be accurately captured. Due to changes in the load (such as load changes during battery charging and discharging, motor starting and running, etc.), fluctuations in current and voltage in the circuit will occur, which may affect the measured insulation resistance value and may lead to inaccurate test results and unable to truly reflect the insulation state of the high-voltage system. The error in the measurement process may be amplified; therefore, after adjusting the resistance, the switching time of the adjustable resistance may lag behind the high-voltage transient change, resulting in missed detection of fast insulation faults. The distributed capacitance and the dynamic resistance network form a resonant circuit, resulting in signal distortion of the measurement. The dynamic resistance switching introduces electromagnetic interference and affects the normal operation of other electronic systems (such as the Controller Area Network CAN bus).

[0023] The present application improves the accuracy of test sampling data through dynamic adjustment of the resistance and multiple pre-tests; ensures the balance of the electric vehicle sampling sample by judging whether to optimize the sample through the confidence interval; realizes the accuracy of the electric vehicle insulation performance test; effectively identifies abnormal situations during the process of dynamically adjusting the resistance through dynamic insulation fault evaluation, improves the effectiveness in the electric vehicle insulation performance test, and can better reflect the actual insulation situation of the electric vehicle.

[0024] Real-time collection of test sampling data, test sampling accurate evaluation parameters, and dynamic insulation fault parameters through big data technology is beneficial to timely identify abnormal situations in the data; through big data technology to analyze the data in the historical database, 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 process of testing the insulation performance of electric vehicles.

[0025] Further, based on the test sampling accurate evaluation parameters in the first insulation performance pre-test process, perform test sampling accurate evaluation to determine whether to dynamically adjust the resistance. The specific method is as follows: A1. Perform relative deviation processing on the resistance value of the insulation resistance in the preset electric vehicle sampling sample and the preset resistance value obtained from the preset database to obtain the resistance tolerance coefficient, that is , where DS represents the resistance tolerance coefficient, DSZ represents the resistance value of the preset electric vehicle sampling sample, which is represented by the average value of the resistance values obtained by the resistance measuring instrument in the electric vehicle during the preset sampling period; represents the preset resistance value, and the preset resistance value is set according to the preset personnel.

[0026] A2. Perform 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 to obtain the input signal amplitude deviation coefficient, 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 actual input signal amplitude is obtained through the analog-to-digital converter; represents the preset input signal amplitude, and the preset input signal amplitude is set according to the preset personnel.

[0027] A3. Introduce a sampling accurate evaluation compensation factor to perform inverse proportional operation on the result of the assignment coupling process of the resistance tolerance coefficient, the input signal amplitude deviation coefficient, and the voltage standard deviation after de-uniting to obtain a sampling accurate evaluation index. The sampling accurate evaluation index is used to quantitatively evaluate the sampling accuracy in the insulation performance pre-test due to resistance differences; the sampling accurate evaluation compensation factor includes a first sampling accurate evaluation compensation factor, a second sampling accurate evaluation compensation factor, and a third sampling accurate evaluation compensation factor; the voltage standard deviation is represented by the standard deviation of the voltage obtained by the voltage sensor in the electric vehicle during the preset sampling period.

[0028] The sampling accuracy evaluation compensation factors involved are obtained from a preset database. The first sampling accuracy evaluation compensation factor represents the influence degree of the resistance value in the electric vehicle on the sampling accuracy evaluation index. The second sampling accuracy evaluation compensation factor represents the influence degree of the input signal amplitude of the analog-to-digital converter in the electric vehicle on the sampling accuracy evaluation index. The third sampling accuracy evaluation compensation factor represents the influence degree of the voltage standard deviation on the sampling accuracy evaluation index. The sum of the three is 1. For example, the resistance value in the electric vehicle and the preset first sampling accuracy evaluation compensation factor form a resistance value mapping set. The real-time resistance value in the electric vehicle is input into the resistance value mapping set to obtain the corresponding first sampling accuracy evaluation compensation factor. The input signal amplitude of the analog-to-digital converter in the electric vehicle and the preset second sampling accuracy evaluation compensation factor form an analog signal amplitude mapping set. The real-time input signal amplitude of the analog-to-digital converter in the electric vehicle is input into the analog signal amplitude mapping set to obtain the corresponding second sampling accuracy evaluation compensation factor. The voltage standard deviation and the preset third sampling accuracy evaluation compensation factor form a voltage standard deviation mapping set. The real-time voltage standard deviation is input into the voltage standard deviation mapping set to obtain the corresponding third sampling accuracy evaluation compensation factor. The mapping relationships therein can be one-to-one or many-to-one relationships.

[0029] Among them, the specific limiting expression of the sampling accuracy evaluation index is: ; In the formula, CZ represents the sampling accuracy evaluation index in the pre-sampling period, UBC represents the voltage standard deviation, represents the first sampling accuracy evaluation compensation factor, represents the second sampling accuracy evaluation compensation factor, represents the third sampling accuracy evaluation compensation factor.

[0030] 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 value of the sampling accuracy evaluation indexes of all preset electric vehicle sampling samples and represents the average sampling accuracy of the insulation performance pre-test. If the average sampling accuracy evaluation index is greater than the preset sampling accuracy evaluation threshold obtained from the preset database, the resistance is dynamically adjusted, where the preset sampling accuracy evaluation threshold is represented by the average value of the sampling accuracy evaluation indexes in the historical time period. If the average sampling accuracy evaluation index is not greater than the preset sampling accuracy evaluation threshold obtained from the preset database, continue the electric vehicle insulation performance test.

[0031] Among them, the dynamic adjustment resistor means adjusting the resistance value of the controllable resistor in the preset high-voltage system of the electric vehicle sampling sample to the optimized resistance value. The optimized resistance value is obtained by inputting the average sampling accurate evaluation index, the preset input signal extreme value of the analog-to-digital converter, and the voltage withstand deviation coefficient into the adjusted resistance value mapping set. The adjusted resistance value mapping set is a set obtained from the preset database, which represents the mapping relationship between the average sampling accurate evaluation index, the preset input signal extreme value of the analog-to-digital converter, the voltage withstand deviation coefficient, and the optimized resistance value.

[0032] Specifically, the voltage withstand 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, they are set 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.

[0033] In this embodiment, the controllable resistor is the controllable resistor in the oversampling circuit within the electric vehicle sampling sample; the main function of the average sampling accurate evaluation index is to reflect the reliability and error of the data during the sampling process. If the sampling accurate evaluation index is lower, it is necessary to improve the sampling accuracy by optimizing the resistance value; the preset input signal extreme values of the analog-to-digital converter affect the conversion from 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, the optimized resistance value should ensure that the input signal remains within the working range of the analog-to-digital converter, which 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 influence of voltage change on current may cause the resistance in the circuit to change.

[0034] The resistor tolerance coefficient may cause gain deviation in the signal conditioning circuit, thus affecting the input signal amplitude; the resistor tolerance coefficient will cause the reference voltage to be unstable, thus 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 is 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 resistor tolerance coefficient, the input signal amplitude deviation coefficient, and the voltage standard deviation, the worse the sampling accuracy in the insulation performance pre-test that may be caused by resistor differences, and the larger the sampling accurate evaluation index may be.

[0035] By performing relative deviation processing on the resistance value of the preset electric vehicle sampling sample and the preset resistance value, the deviation degree 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 sampling sample and the preset input signal amplitude, the deviation of the actual input signal amplitude from the preset value is reflected, and the accuracy of the signal during the conversion process is evaluated; through the above steps, the sampling accuracy in the pre-test of the insulation performance caused by resistance differences is quantitatively evaluated, providing a basis for the subsequent dynamic adjustment of the resistance, thereby effectively reducing the sampling error caused by circuit element differences, improving the sampling accuracy, and ensuring the stability of the electric vehicle insulation performance test. By dynamically adjusting the resistance, it is ensured that the electric vehicle insulation performance test remains in the optimal state, thereby improving the accuracy of the insulation performance test; by considering the voltage withstand deviation coefficient and the preset input signal extreme value, the accurate conversion and transmission of the signal in the electric vehicle insulation performance test are ensured.

[0036] Further, based on the second insulation resistance value error, it is judged whether to perform dynamic insulation fault assessment. The specific process is as follows: Deviation processing is performed on the second insulation resistance value and the preset insulation resistance value to obtain the second insulation resistance value error; it is judged 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, dynamic insulation fault assessment is performed according to the dynamic insulation fault parameters during the process of dynamically adjusting the resistance; the preset insulation resistance value error is set according to the preset personnel, for example, represented by the average value of the insulation resistance value errors within the historical time period. If the second insulation resistance value error is not greater than the preset insulation resistance value error obtained from the preset database, it means that the second test sampling data of the preset electric vehicle sampling sample is qualified. After obtaining the second test sampling data by performing the second insulation performance pre-test on the preset electric vehicle sampling sample, it is judged whether to optimize the test sampling sample based on the confidence interval of the second test sampling data.

[0037] Among them, dynamic insulation fault assessment is performed according to the dynamic insulation fault parameters during the process of dynamically adjusting the resistance. The specific method is as follows: B1, if the resistance adjustment duration is less than the high-voltage transient response duration obtained from the preset database, then deviation comparison processing is performed on the resistance adjustment duration and the high-voltage transient response duration obtained from the preset database to obtain the resistance adjustment deviation coefficient, otherwise the resistance adjustment deviation coefficient is recorded as 0; the specific limiting expression of the resistance adjustment deviation coefficient is: ; In the formula, RQ represents the resistance adjustment deviation coefficient, RQT represents the resistance adjustment duration, and represents the time required for the measured resistance value to change using a high-speed oscilloscope; It represents the high-voltage transient response duration, which is set according to a preset person. For example, it is set according to the high-voltage transient response requirement (such as 100 μs).

[0038] B2. If the fault detection delay is less than the preset fault detection delay obtained from the preset database, then 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. The specific limiting expression of the fault detection delay deviation coefficient is: ; In the formula, GY represents the fault detection delay deviation coefficient, GYC represents the fault detection delay, and represents the time required from the occurrence of the fault to the detection of the fault recorded in the electric vehicle insulation performance test system; represents the preset fault detection delay, which is set according to a preset person. For example, it is set according to the time of simulated fast insulation fault detection (such as 100 ns).

[0039] B3. A comparison process is performed based on the signal distortion rate and the preset signal distortion rate obtained from the preset database to obtain a signal distortion rate comparison coefficient, that is , where XJB represents the signal distortion rate, which represents the degree of distortion that occurs during the transmission or processing of the signal and is obtained by comparing the distortion degrees of the measured signals before and after resonance (such as sine wave distortion); in the formula represents the preset signal distortion rate, which is set according to a preset person. For example, it is represented by the average value of the signal distortion rate within a historical time period.

[0040] B4. A comparison process is performed based on the conducted noise and the preset conducted noise obtained from the preset database to obtain a conducted noise comparison coefficient, that is , where CGZ represents the conducted noise, and the conducted noise is represented by the noise on the power line measured by a line impedance stabilization network; represents the preset conducted noise, which is set according to a preset person. For example, it is represented by the average value of the conducted noise within a historical time period.

[0041] B5. A dynamic fault compensation factor is introduced to perform an assignment coupling process on the resistance adjustment deviation coefficient, the fault detection delay deviation coefficient, the de-normalized standard deviation of the DC voltage, the signal distortion rate comparison coefficient, and the conducted noise comparison coefficient to obtain a dynamic insulation fault index, which is used to quantitatively evaluate the abnormality degree of the insulation performance test after the dynamic adjustment of the resistance; the dynamic fault compensation factor includes 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.

[0042] Among them, the specific limit expression of the dynamic insulation fault index is: ; In the formula, DT represents the dynamic insulation fault index, RQ represents the resistance adjustment deviation coefficient, DYB represents the standard deviation of the DC voltage during the DC voltage injection process, 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.

[0043] The above dynamic fault compensation factors are obtained from a preset database. The first dynamic fault compensation factor represents the influence degree of the resistance adjustment duration on the dynamic insulation fault index, the second dynamic fault compensation factor represents the influence degree of the fault detection delay on the dynamic insulation fault index, the third dynamic fault compensation factor represents the influence degree of the standard deviation of the DC voltage during the DC voltage injection process on the dynamic insulation fault index, the fourth dynamic fault compensation factor represents the influence degree of the signal distortion rate on the dynamic insulation fault index, and the fifth dynamic fault compensation factor represents the influence degree of the conducted noise on the dynamic insulation fault index; the sum of the five is 1. For example, the resistance adjustment duration and the preset first dynamic fault compensation factor form a resistance adjustment duration mapping set, and the real-time resistance adjustment duration is input into the resistance adjustment duration 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 fault detection delay mapping set, and the real-time fault detection delay is input into the fault detection delay mapping set to obtain the corresponding second dynamic fault compensation factor; the standard deviation of the DC voltage during the DC voltage injection process and the preset third dynamic fault compensation factor form a standard deviation of the DC voltage mapping set, and the real-time standard deviation of the DC voltage during the DC voltage injection process is input into the standard deviation of the DC voltage 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 therein can be a one-to-one or many-to-one relationship.

[0044] In this embodiment, by monitoring the resistance value error of the second insulation resistance, insulation faults can be detected in a timely manner, providing a screening mechanism for subsequent dynamic insulation faults, avoiding unnecessary dynamic evaluations, and thus improving the efficiency of the insulation performance test for electric vehicles. In the algorithm of this embodiment, the larger the resistance adjustment deviation coefficient, the more likely it is to cause a delay in triggering the fault signal, thereby increasing the fault detection delay; the larger the conduction noise, the more likely it is to cause signal distortion, thereby increasing the signal distortion rate; the larger the signal distortion rate, the more likely it is to cause misjudgment of the fault signal, thereby increasing the fault detection delay; the larger the conduction noise, the greater the possible fault detection delay; the larger the resistance adjustment deviation coefficient, the more noise may be introduced, resulting in an increase in the signal distortion rate; the conduction noise will be directly superimposed on the DC voltage signal, resulting in an increase in the standard deviation of the DC voltage during the DC voltage injection process; the larger the standard deviation of the DC voltage, the more likely it is to cause misjudgment of the fault signal, thereby increasing the fault detection delay; the longer the resistance adjustment duration, the fault detection delay, the standard deviation of the DC voltage, the signal distortion rate, and the conduction noise, the more likely it is to cause a larger dynamic insulation fault index.

[0045] Through the evaluation of the resistance adjustment deviation coefficient, the timeliness of the resistance adjustment is reflected; through the evaluation of the fault detection delay deviation coefficient, the response delay of the insulation fault detection of the electric vehicle is measured, avoiding an increase in the degree of insulation fault caused by the response delay; through the evaluation of the signal distortion rate comparison coefficient, the degree of distortion of the data during transmission or processing is quantified; through the evaluation of the conduction noise comparison coefficient, the degree of noise interference in the insulation performance test signal of the electric vehicle is measured; through the above steps, the abnormality degree of the insulation performance test after the dynamic adjustment of the resistance is quantitatively evaluated, effectively correcting the abnormality caused by the dynamic adjustment of the resistance, improving the dynamic response ability of the insulation performance test of the electric vehicle, and improving the stability of the insulation performance test of the electric vehicle.

[0046] Furthermore, the specific process of optimizing the dynamic insulation performance test according to the results of the dynamic insulation fault evaluation is as follows: C1. Determine 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, increase the damping resistance; otherwise, directly prompt 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 within the historical time period.

[0047] The damping resistance is increased by adjusting the resistance value of the resistor connected in series 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 the load parameters into the resonant resistance mapping set. The resonant resistance mapping set is a set representing the mapping relationship between the dynamic insulation fault index, the load parameters, and the resonant resistance value, which is obtained from a preset database. The load parameters include current and voltage. The damping resistance is a resistor used to regulate the circuit in an electrical system, usually used to reduce the fluctuation of the current and stabilize the system. The resonant resistance refers to the resistance value associated with the resonant circuit in the circuit.

[0048] C2, determine whether the dynamic insulation fault index after the increase of the damping resistance is greater than the preset dynamic insulation fault threshold obtained from the preset database. If the dynamic insulation fault index after the increase of the damping resistance is not greater than the preset dynamic insulation fault threshold obtained from the preset database, then optimize the resistance adjustment duration, otherwise end the optimization of the dynamic insulation performance test.

[0049] C3, determine whether the dynamic insulation fault index after the optimization of the resistance adjustment duration is greater than the preset dynamic insulation fault threshold obtained from the preset database. If the dynamic insulation fault index after the optimization of the resistance adjustment duration is not greater than the preset dynamic insulation fault threshold obtained from the preset database, then perform filtering optimization, otherwise end the optimization of the dynamic insulation performance test.

[0050] C4, determine whether the dynamic insulation fault index after the filtering optimization is greater than the preset dynamic insulation fault threshold obtained from the preset database. If the dynamic insulation fault index after the filtering optimization is not greater than the preset dynamic insulation fault threshold obtained from the preset database, then perform output frequency adjustment, otherwise end the optimization of the dynamic insulation performance test.

[0051] C5, determine whether the dynamic insulation fault index after the output frequency adjustment is greater than the preset dynamic insulation fault threshold obtained from the preset database. 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, then perform feedback, otherwise end the optimization of the dynamic insulation performance test.

[0052] After ending the optimization of the dynamic insulation performance test, after obtaining the third test sampling data by performing the third pre-insulation performance test on the preset electric vehicle sampling sample, determine whether to optimize the test sampling sample based on the confidence interval of the third test sampling data.

[0053] The optimization of the dynamic insulation performance test involved includes increasing the damping resistance, optimizing the resistance adjustment duration, filtering optimization, and output frequency adjustment.

[0054] The 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 load parameters into the output frequency mapping set. The output frequency mapping set is a set representing the mapping relationship between the dynamic insulation fault index, load parameters, and the adjusted output frequency, which is obtained from a preset database.

[0055] The optimization of the resistance adjustment duration includes using a Solid State Relay (SSR) and pre-switching setting. By using the solid state relay, the resistance adjustment duration can be shortened to the 10 μs level.

[0056] The pre-switching setting means determining whether to trigger pre-switching based on the voltage change rate. Determining whether to trigger 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 is switched to the preset low-impedance circuit; otherwise, the electric vehicle insulation performance test continues on the current circuit. The preset voltage change rate is set according to preset personnel, for example, represented by the average value of the voltage change amount. The preset low-impedance circuit is set according to preset personnel.

[0057] The filtering optimization includes cut-off frequency adjustment and inductance-capacitance adjustment.

[0058] The cut-off frequency adjustment means setting the cut-off frequency of the filter to the optimized cut-off frequency. The optimized cut-off frequency is obtained by inputting the dynamic insulation fault index and resonance frequency into the cut-off frequency mapping set. The cut-off frequency mapping set is a set representing the mapping relationship between the dynamic insulation fault index, resonance frequency, and the optimized cut-off frequency, which is obtained from a preset database.

[0059] The inductance-capacitance 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 of the inductor and load parameters into the inductor mapping set. The capacitance value of the differential-mode capacitor is obtained by inputting the transient resonance of the capacitor and load parameters into the capacitor mapping set. The inductor mapping set is a set representing the mapping relationship between the transient resonance of the inductor, load parameters, and the inductance value of the common-mode inductor, which is obtained from a preset database. The capacitor mapping set is a set representing the mapping relationship between the transient resonance of the capacitor, load parameters, and the capacitance value of the differential-mode capacitor, which is obtained from a preset database. The common-mode inductor refers to the inductor used to suppress common-mode interference in the circuit. Common-mode interference refers to the interference signal with the same voltage change on two signal lines. The differential-mode capacitor refers to the capacitor used to suppress differential-mode interference in the circuit. Differential-mode interference refers to the interference signal with different voltage changes between two signal lines.

[0060] In this embodiment, as Figure 2As shown in the figure, it is a flowchart of dynamic insulation fault assessment, judgment, and optimization provided by the embodiment of the present application. After dynamic insulation fault assessment, it is judged whether the dynamic insulation fault index is greater than the preset dynamic insulation fault threshold. If not, the damping resistance is increased. Otherwise, it is directly prompted that the insulation performance test of the preset personnel is abnormal. It is judged whether the dynamic insulation fault index after the damping resistance is increased is greater than the preset dynamic insulation fault threshold. If not, the optimization of the resistance adjustment duration is carried out. Otherwise, the insulation performance test is carried out. It is judged whether the dynamic insulation fault index after the optimization of the resistance adjustment duration is greater than the preset dynamic insulation fault threshold. If not, the filtering optimization is carried out. Otherwise, the insulation performance test is carried out. It is judged whether the dynamic insulation fault index after the filtering optimization is greater than the preset dynamic insulation fault threshold. If not, the output frequency adjustment is carried out. Otherwise, the insulation performance test is carried out. It is judged whether the dynamic insulation fault index after the output frequency adjustment is greater than the preset dynamic insulation fault threshold. If not, the feedback is carried out. Otherwise, the insulation performance test is carried out.

[0061] By real-time monitoring and optimizing the dynamic insulation fault index, the accuracy of the insulation performance test of electric vehicles can be effectively improved; through the optimization of the dynamic insulation performance test, the insulation performance of electric vehicles can be optimized in real time, the incidence of insulation faults can be reduced, and a good insulation state can be ensured under different working conditions.

[0062] By increasing the damping resistance, the current fluctuation in the high-voltage system of electric vehicles is reduced, and the stability of the insulation performance test of electric vehicles is improved; by optimizing the resistance adjustment duration parameter, more accurate adjustment of the insulation performance of electric vehicles is ensured; by optimizing the parameter settings of the filter, noise can be reduced and the acquisition accuracy of test sampling data in the insulation performance test of electric vehicles can be improved; by increasing the common-mode inductor, common-mode interference can be reduced; by increasing the differential-mode capacitor, differential-mode interference can be suppressed; thus, the accuracy of the insulation performance test of electric vehicles is achieved, and errors caused by signal interference or electrical instability are reduced; by automatically adjusting parameters through the mapping relationship in the preset database, the efficiency of the insulation performance test of electric vehicles is improved.

[0063] Furthermore, the specific process of judging whether to optimize the test sampling sample based on the confidence interval of the third test sampling data is as follows: Confidence processing is carried out according to the average value and standard deviation of the insulation resistance value in the third test sampling data to obtain the confidence interval.

[0064] It is judged whether the width of the confidence interval is greater than the preset confidence interval width obtained from the preset database. If the width of the confidence interval is greater than the preset confidence interval width obtained from the preset database, the sampling sample generation is carried out. Otherwise, the test sampling sample is not optimized; the preset confidence interval width is set according to the preset personnel.

[0065] Determine whether the width of the confidence interval after the sampling sample is generated is greater than the preset confidence interval width obtained from the preset database. If the width of the confidence interval after the sampling sample is generated is greater than the preset confidence interval width obtained from the preset database, perform sampling frequency adjustment; otherwise, end the optimization of the test sampling sample.

[0066] Determine whether the width of the confidence interval after the sampling frequency is adjusted is greater than the preset confidence interval width obtained from the preset database. If the width of the confidence interval after the sampling frequency is adjusted is greater than the preset confidence interval width obtained from the preset database, give feedback; otherwise, end the optimization of the test sampling sample.

[0067] The optimization of the test sampling sample 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 sample according to the load parameters of the preset sampling sample 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, find its k nearest neighbors (usually k = 5), then randomly select a sample from the k nearest neighbors, and generate a new minority class sample according to the difference in feature vectors between these two samples and a certain ratio (usually a random number less than 1).

[0068] 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 resistance value error of the optimized insulation resistance in the dynamic insulation performance test into the sampling frequency mapping set. The sampling frequency mapping set is a set obtained from the preset database that represents the mapping relationship between the sampling accuracy evaluation index, the number of test samples, the resistance value error of the optimized insulation resistance in the dynamic insulation performance test, and the optimized test sampling frequency.

[0069] 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 sampling sample generation and sampling frequency adjustment, the diversity and richness of the samples can be increased, the influence 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 mistakes in dynamic resistance adjustment and dynamic insulation performance test optimization caused by incomplete test sampling data, and thus achieving the accuracy of the electric vehicle insulation performance test.

[0070] By generating new sampling samples through the SMOTE algorithm, the diversity of the sampling samples is increased, enabling the sampling samples to better cover the feature space of the data, thereby improving the comprehensiveness of the test sampling data in the insulation performance test of electric vehicles; by optimizing the sampling frequency, the accuracy and integrity of the test sampling data are improved, and thus the accuracy of the insulation performance test of electric vehicles is achieved.

[0071] Such as Figure 3As shown in the figure, it is a schematic structural diagram of a big data-based electric vehicle insulation performance test system provided by an embodiment of the present application. The system of a big data-based electric vehicle insulation performance test method provided by an embodiment of the present application includes: a test sampling accurate 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; among them, the test sampling accurate 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 accurate evaluation according to the test sampling accurate evaluation parameters in the process of performing the first insulation performance pre-test, and judge whether to dynamically adjust the resistance; the first insulation resistance value error judgment module is used to, if the resistance is not dynamically adjusted, obtain a first insulation resistance value by injecting a preset DC voltage into the electric vehicle high-voltage system, and perform a deviation process on the first insulation resistance value and the preset insulation resistance value to obtain a first insulation resistance value error, judge whether to perform feedback based on the first insulation resistance value error, and judge whether to optimize the test sampling sample based on the confidence interval of the first test sampling data. If the test sampling sample is not optimized, perform an insulation performance test on the preset electric vehicle sampling sample, otherwise perform an insulation performance test on the preset electric vehicle sampling sample after optimizing the test sampling sample; the second insulation resistance value error judgment module is used to, if the resistance is dynamically adjusted, obtain a second insulation resistance value by injecting a preset DC voltage into the electric vehicle high-voltage system after dynamically adjusting the resistance, and perform a deviation process on the second insulation resistance value and the preset insulation resistance value to obtain a second insulation resistance value error, judge whether to perform a dynamic insulation fault evaluation based on the second insulation resistance value error. If the dynamic insulation fault evaluation is not performed, perform a second insulation performance pre-test on the preset electric vehicle sampling sample to obtain second test sampling data, and judge whether to optimize the test sampling sample based on the confidence interval of the second test sampling data, otherwise execute the function of the dynamic insulation fault evaluation judgment module; the dynamic insulation fault evaluation judgment module is used to, if the dynamic insulation fault evaluation is performed, perform dynamic insulation performance test optimization according to the result of the dynamic insulation fault evaluation, and then perform a third insulation performance pre-test on the preset electric vehicle sampling sample to obtain third test sampling data, and judge whether to optimize the test sampling sample based on the confidence interval of the third test sampling data. If the test sampling sample is not optimized, perform an insulation performance test on the preset electric vehicle sampling sample, otherwise perform an insulation performance test on the preset electric vehicle sampling sample after optimizing the test sampling sample.

[0072] In this embodiment, by Figure 3It can be seen that after the function of the test sampling accuracy evaluation module is executed, the function of the first insulation resistance value error judgment module may be executed, or the function of the second insulation resistance value error judgment module may be executed. After the function of the test sampling accuracy evaluation module is executed, the function of the dynamic insulation fault evaluation judgment module is not necessarily executed.

[0073] 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 required, thus laying a foundation for accurately measuring the insulation resistance value subsequently and effectively improving the accuracy of test sampling; through the first insulation resistance value error judgment module and the second insulation resistance value error judgment module, abnormal situations in the measurement process can be discovered and optimized in time, thus 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; furthermore, the improvement of the accuracy of the electric vehicle insulation performance test is realized.

[0074] In summary, in the embodiment of the present application, it is judged whether to dynamically adjust the resistance based on the result of the test sampling accuracy evaluation, then it is judged whether to perform dynamic insulation fault evaluation based on the insulation resistance value error after dynamically adjusting the resistance, and the dynamic insulation performance test is optimized according to the result of the dynamic insulation fault evaluation. Finally, it is judged 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 further realizing the improvement of the accuracy of the electric vehicle insulation performance test, effectively solving the problem of low accuracy of the electric vehicle insulation performance test caused by sampling data error in the prior art.

[0075] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for realizing the processFigure 1 means for the functions specified in one process or multiple processes and / or boxes Figure 1 or multiple boxes.

[0077] These computer program instructions can 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 a manufactured article including an instruction device, and the instruction device implements the process Figure 1 means for the functions specified in one process or multiple processes and / or boxes Figure 1 or multiple boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the process Figure 1 means for the functions specified in one process or multiple processes and / or boxes Figure 1 or multiple boxes.

[0079] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0080] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for testing the insulation performance of electric vehicles based on big data, characterized in that, It includes the following steps: S1. During the pre-sampling period, conduct the first pre-test of the insulation performance on the preset electric vehicle sampling sample to obtain the first test sampling data. At the same time, accurately evaluate the test sampling according to the test sampling accurate evaluation parameters in the process of the first insulation performance pre-test, and determine whether to dynamically adjust the resistance; S2. If the resistance is not dynamically adjusted, obtain the first insulation resistance value error from the first insulation resistance value obtained by injecting a preset DC voltage into the high-voltage system of the electric vehicle, and determine whether to optimize the test sampling sample based on the confidence interval of the first test sampling data. If the test sampling sample is not optimized, conduct the insulation performance test on the preset electric vehicle sampling sample. Otherwise, conduct the insulation performance test after optimizing the test sampling sample; S3. If the resistance is dynamically adjusted, obtain the second insulation resistance value error from the second insulation resistance value obtained by injecting a preset DC voltage into the high-voltage system of the electric vehicle after dynamically adjusting the resistance. Based on the second insulation resistance value error, determine whether to conduct a dynamic insulation fault assessment. If not, conduct the second pre-test of the insulation performance on the preset electric vehicle sampling sample to obtain the second test sampling data, and then determine whether to optimize the test sampling sample based on the confidence interval of the second test sampling data. Otherwise, execute S4; S4. If a dynamic insulation fault assessment is conducted, after optimizing the dynamic insulation performance test according to the result of the dynamic insulation fault assessment, conduct the third pre-test of the insulation performance on the preset electric vehicle sampling sample to obtain the third test sampling data, and then determine whether to optimize the test sampling sample based on the confidence interval of the third test sampling data. If not, conduct the insulation performance test on the preset electric vehicle sampling sample. Otherwise, conduct the insulation performance test after optimizing the test sampling sample.

2. The method for testing the insulation performance of an electric vehicle based on big data according to claim 1, characterized in that: The specific method for accurately evaluating the test sampling according to the test sampling accurate evaluation parameters in the process of the first insulation performance pre-test is as follows: Conduct relative deviation processing on the resistance value of the insulation resistance in the preset electric vehicle sampling sample and the preset resistance value obtained from the preset database to obtain the resistance tolerance coefficient; Conduct 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 to obtain the input signal amplitude deviation coefficient; Introduce a sampling accurate evaluation compensation factor to perform an inverse proportional operation on the result of the assignment coupling process of the resistance tolerance coefficient, the input signal amplitude deviation coefficient, and the de-unified voltage standard deviation to obtain the sampling accurate evaluation index. The sampling accurate evaluation index is used to quantitatively evaluate the sampling accuracy in the insulation performance pre-test caused by resistance differences.

3. The method for testing the insulation performance of an electric vehicle based on big data according to claim 2, characterized in that: The specific process for determining whether to dynamically adjust the resistance is as follows: Determine whether the average sampling accurate evaluation index is greater than the preset sampling accurate evaluation threshold obtained from the preset database: The average sampling accurate evaluation index represents the average sampling accuracy of the insulation performance pre-test; If the average sampling accurate evaluation index is greater than the preset sampling accurate evaluation threshold obtained from the preset database, dynamically adjust the resistance; If the average sampling accuracy evaluation index is not greater than the preset sampling accuracy evaluation threshold obtained from the preset database, continue the electric vehicle insulation performance test; The dynamic adjustment resistor 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 deviation coefficient into the adjusted resistance value mapping set. The adjusted resistance value mapping set is a set representing 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 deviation coefficient, and the optimized resistance value, which is obtained from the preset database; The voltage withstand 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.

4. The method for testing the insulation performance of an electric vehicle based on big data according to claim 1, wherein: The method for determining whether to perform dynamic insulation fault evaluation based on the second insulation resistance value error is as follows: Perform deviation processing on the second insulation resistance value and the preset insulation resistance value to obtain the 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, perform dynamic insulation fault evaluation according to the dynamic insulation fault parameters during the process of dynamic adjustment of the resistor; If the second insulation resistance value error is not greater than the preset insulation resistance value error obtained from the preset database, after performing the second insulation performance pre-test on the preset electric vehicle sampling sample to obtain the second test sampling data, determine whether to optimize the test sampling sample based on the confidence interval of the second test sampling data.

5. The method for testing the insulation performance of an electric vehicle based on big data according to claim 4, characterized in that: The method for performing dynamic insulation fault evaluation according to the dynamic insulation fault parameters during the process of dynamic adjustment of the resistor is as follows: If the resistor adjustment duration is less than the high-voltage transient response duration obtained from the preset database, perform deviation comparison processing on the resistor adjustment duration and the high-voltage transient response duration obtained from the preset database to obtain the resistor adjustment deviation coefficient; otherwise, record the resistor adjustment deviation coefficient as 0; If the fault detection delay is less than the preset fault detection delay obtained from the preset database, perform deviation comparison processing on the fault detection delay and the preset fault detection delay obtained from the preset database to obtain the fault detection delay deviation coefficient; otherwise, record the fault detection delay deviation coefficient as 0; Perform comparison processing on the signal distortion rate and the preset signal distortion rate obtained from the preset database to obtain the signal distortion rate comparison coefficient; Perform comparison processing on the conducted noise and the preset conducted noise obtained from the preset database to obtain the conducted noise comparison coefficient; A dynamic fault compensation factor is introduced to perform assignment coupling processing on the resistance adjustment deviation coefficient, the fault detection delay deviation coefficient, the standard deviation of the DC voltage after de-unitarization, the signal distortion rate comparison coefficient, and the conducted noise comparison coefficient, obtaining a dynamic insulation fault index, which is used to quantitatively evaluate the abnormality degree of the insulation performance test after the dynamic adjustment of the resistance.

6. The method for testing the insulation performance of an electric vehicle based on big data according to claim 5, wherein: The specific process of optimizing the dynamic insulation performance test according to 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 are directly prompted that the insulation performance test is abnormal. The increase in the damping resistance is achieved by adjusting the resistance value of the resistor in series 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 the load parameters into the resonant resistance mapping set. The resonant resistance mapping set is a set representing the mapping relationship between the dynamic insulation fault index, the load parameters, and the resonant resistance value, which is obtained from the preset database. The load parameters include current and voltage. If the dynamic insulation fault index after the increase in the damping resistance is not greater than the preset dynamic insulation fault threshold obtained from the preset database, the resistance adjustment duration is optimized; otherwise, the optimization of the dynamic insulation performance test ends. If the dynamic insulation fault index after the optimization of the resistance adjustment duration is not greater than the preset dynamic insulation fault threshold obtained from the preset database, the filtering is optimized; otherwise, the optimization of the dynamic insulation performance test ends. If the dynamic insulation fault index after the filtering optimization is not greater than the preset dynamic insulation fault threshold obtained from the preset database, the output frequency is adjusted; otherwise, the optimization of the dynamic insulation performance test ends. 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 performed; otherwise, the optimization of the dynamic insulation performance test ends. The output frequency adjustment means adjusting the working frequency of the electric vehicle insulation performance test to the adjusted output frequency, which is obtained by inputting the dynamic insulation fault index and the load parameters into the output frequency mapping set. The output frequency mapping set is a set representing the mapping relationship between the dynamic insulation fault index, the load parameters, and the adjusted output frequency, which is obtained from the preset database.

7. The method for testing the insulation performance of an electric vehicle based on big data according to claim 6, characterized in that: The optimization of the dynamic insulation performance test includes increasing the damping resistance, optimizing the resistance adjustment duration, optimizing the filtering, and adjusting the output frequency. The optimization of the resistance adjustment duration includes using a solid-state relay and pre-switching setting. The pre-switching setting means judging whether to trigger pre-switching based on the voltage change rate. Judging whether to trigger 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 loop is switched to the preset low-impedance loop; otherwise, the electric vehicle insulation performance test continues on the current loop. The filtering optimization includes cut-off frequency adjustment and inductance-capacitance adjustment. The cut-off frequency adjustment means setting the cut-off frequency of the filter to the optimized cut-off frequency, which is obtained by inputting the dynamic insulation fault index and the resonance frequency into the cut-off frequency mapping set. The cut-off frequency mapping set is a set representing the mapping relationship between the dynamic insulation fault index, the resonance frequency, and the optimized cut-off frequency, which is obtained from a preset database; The inductance-capacitance 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 of the inductor and the load parameters into the inductor mapping set. The capacitance value of the differential-mode capacitor is obtained by inputting the transient resonance of the capacitor and the load parameters into the capacitor mapping set. The inductor mapping set is a set representing the mapping relationship between the transient resonance of the inductor, the load parameters, and the inductance value of the common-mode inductor, which is obtained from a preset database. The capacitor mapping set is a set representing the mapping relationship between the transient resonance of the capacitor, the load parameters, and the capacitance value of the differential-mode capacitor, which is obtained from a preset database.

8. The method for testing the insulation performance of an electric vehicle based on big data according to claim 1, characterized in that: The specific process of determining whether to optimize the test sampling samples based on the confidence interval of the third test sampling data is as follows: Perform confidence processing on the average value and standard deviation of the third insulation resistance value to obtain a confidence interval; Judge whether the width of the confidence interval is greater than the preset confidence interval width obtained from the preset database. If the width of the confidence interval is greater than the preset confidence interval width obtained from the preset database, generate sampling samples, otherwise do not optimize the test sampling samples; If the width of the confidence interval after generating the sampling samples is greater than the preset confidence interval width obtained from the preset database, adjust the sampling frequency, otherwise end the optimization of the test sampling samples; If the width of the confidence interval after adjusting the sampling frequency is greater than the preset confidence interval width obtained from the preset database, give feedback, otherwise end the optimization of the test sampling samples.

9. The method for testing the insulation performance of an electric vehicle based on big data according to claim 8, characterized in that: The optimization of the test sampling samples includes generating sampling samples and adjusting the sampling frequency; The generation of sampling samples means using the 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 adjustment of the sampling frequency means adjusting the current test sampling frequency to the optimized test sampling frequency, which is obtained by inputting the sampling accuracy evaluation index, the number of test samplings, and the insulation resistance value error after optimizing the dynamic insulation performance test into the sampling frequency mapping set. The sampling frequency mapping set is a set representing the mapping relationship between the sampling accuracy evaluation index, the number of test samplings, the insulation resistance value error after optimizing the dynamic insulation performance test, and the optimized test sampling frequency, which is obtained from a preset database.

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

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