Electric vehicle high voltage system detection method and electronic equipment

Abstract

The present application relates to the field of insulation detection technology, and discloses a method and electronic device for detecting a high-voltage system of an electric vehicle. The method comprises: obtaining voltage sampling data obtained by sampling an insulation detection system; performing voltage waveform classification processing on the voltage sampling data to obtain a classification result; according to the classification result, using a nonlinear least squares estimation algorithm, performing adaptive identification processing on the voltage sampling data to obtain an identification result; using the identification result as an observation quantity, performing state quantity estimation processing on a state space model with the insulation resistance value and the Y capacitor capacitance as state quantities, and obtaining an estimated resistance value of the insulation resistance and an estimated capacitance value of the Y capacitor. The embodiment of the present application can improve the detection accuracy of the insulation resistance and the Y capacitor.

Description

Technical Field

[0001] The present application relates to the technical field of insulation detection, and in particular to a detection method and electronic equipment for a high-voltage system of an electric vehicle. Background Art

[0002] At present, the insulation resistance of electric vehicles is mainly tested by passive and active methods.

[0003] In the related art, the passive method usually adopts the bridge method, which specifically comprises forming a bridge between the resistor to be measured and a known resistor, measuring the voltage divider on the resistor to be measured by changing the size of the current limiting resistor, and finally calculating the insulation resistance value by solving the equation.

[0004] However, due to the existence of Y capacitance between the two poles of the power battery and the electric chassis, the electrical structure of the electric vehicle will lead to the generation of parasitic Y capacitance, which will cause changes in impedance characteristics, affect the insulation performance between the high-voltage bus and the chassis, and affect the detection accuracy of insulation resistance and Y capacitance. Summary of the invention

[0005] The purpose of this application is to provide a method and electronic equipment for detecting a high voltage system of an electric vehicle, which can improve the detection accuracy of insulation resistance and Y capacitance.

[0006] The present application embodiment provides a method for detecting a high voltage system of an electric vehicle, comprising:

[0007] Acquire voltage sampling data obtained by sampling by the insulation detection system; the voltage sampling data is voltage waveform data obtained by the insulation detection system performing periodic voltage sampling on the electric vehicle high voltage system using an unbalanced bridge method, and the electric vehicle high voltage system has an insulation resistance and a Y capacitor;

[0008] Performing voltage waveform classification processing on the voltage sampling data to obtain a classification result;

[0009] According to the classification result, a nonlinear least square estimation algorithm is used to perform adaptive identification processing on the voltage sampling data to obtain an identification result; the identification result includes response steady-state component information and response time information of the voltage sampling data;

[0010] Taking the identification result as the observed quantity, a state quantity estimation process is performed on a state space model taking the resistance value of the insulation resistor and the capacitance value of the Y capacitor as the state quantity to obtain the resistance estimation value of the insulation resistor and the capacitance estimation value of the Y capacitor.

[0011] In some embodiments, the voltage waveform classification processing is performed on the voltage sampling data to obtain a classification result, including:

[0012] Performing waveform slope estimation processing on the voltage sampling data to obtain a slope estimation value;

[0013] When the slope estimation value is in the first slope interval, the voltage sampling data is classified as an exponential function wave; when the slope estimation value is in the second slope interval, the voltage sampling data is classified as a critical square wave; when the slope estimation value is in the third slope interval, the voltage sampling data is classified as a square wave; the critical values ​​of the first slope interval, the second slope interval and the third slope interval increase in sequence.

[0014] In some embodiments, the adaptive identification processing of the voltage sampling data is performed using a nonlinear least squares estimation algorithm according to the classification result to obtain an identification result, including:

[0015] According to the classification result, a difference function is constructed; the difference function is fitted by an exponential function vector and a voltage sampling value, the exponential function vector has a voltage value vector, when the voltage sampling data is an exponential function wave, the voltage value vector is composed of a preset first response steady-state component, a first response gain component and a first response time constant, when the voltage sampling data is a critical square wave, the voltage value vector is composed of a second response steady-state component, a second response gain component and a second response time constant, when the voltage sampling data is a square wave, the voltage value vector is composed of a second response steady-state component, the second response steady-state component is generated by an average voltage value at the end stage of the current half sampling period, the second response gain component is generated by a preset constant, and the second response time constant is fitted by the second response steady-state component, the structural parameters of the electric vehicle high-voltage system and the insulation detection system;

[0016] Adopting a nonlinear least squares estimation algorithm to iteratively update the voltage value vector, so that the error of the difference function is iteratively reduced until a preset convergence condition is met, thereby obtaining an iterated voltage value vector;

[0017] A measurement matrix is ​​constructed using the iterated voltage value vector to serve as the identification result.

[0018] In some embodiments, when the voltage sampling data is an exponential function wave, the expression of the difference function is:

[0019] ,

[0020] When the voltage sampling data is a critical square wave, the expression of the difference function is:

[0021] ,

[0022] When the voltage sampling data is a square wave, the expression of the difference function is:

[0023] ,

[0024] in, is the ith difference function of the current half sampling period, is the i-th exponential function vector of the current half sampling period, is the first response steady-state component of the current half sampling period, is the first response gain component of the current half sampling period, is the first response time constant of the current half sampling period, is the i-th sampling moment of the current half sampling period, is the i-th voltage sampling value of the current half sampling period, is the second response steady-state component of the current half sampling period, is the second response gain component of the current half sampling period, is the second response time constant of the current half sampling period.

[0025] In some embodiments, the nonlinear least squares estimation algorithm is used to iteratively update the voltage value vector so that the error of the difference function is iteratively reduced until a preset convergence condition is met, and the iterated voltage value vector is obtained, including:

[0026] Generate a voltage value vector for the current iteration cycle;

[0027] According to the voltage value vector of the current iteration cycle, the current error of the difference function is calculated, and a Jacobian matrix and a quasi-Hessian matrix are constructed;

[0028] Calculating a correction amount according to the Jacobian matrix and the quasi-Hessian matrix, and using the correction amount to correct the voltage value vector of the current iteration cycle to obtain a corrected difference function;

[0029] Calculating a corrected error of the difference function according to the corrected difference function;

[0030] Determining whether the corrected error is less than the current error;

[0031] If it is not less than the current error, adjusting the correction coefficient; returning to the step of calculating the correction amount according to the Jacobian matrix and the quasi-Hessian matrix, and using the correction amount to correct the voltage value vector of the current iteration cycle to obtain a corrected voltage value vector;

[0032] If it is smaller than the current error, determining whether the norm of the difference between the voltage value vector corrected in the current iteration cycle and the voltage value vector corrected in the previous iteration cycle is smaller than a preset termination accuracy value;

[0033] If it is less than the termination accuracy value, the voltage value vector of the current iteration cycle is used as the voltage value vector after the iteration;

[0034] If it is not less than the termination accuracy value, adjust the correction coefficient and use the next iteration cycle as the current iteration cycle; return to the step of generating the voltage value vector of the current iteration cycle.

[0035] In some embodiments, taking the identification result as an observation quantity, performing state quantity estimation processing on a state space model taking the resistance value of the insulation resistor and the capacitance value of the Y capacitor as state quantities to obtain the estimated resistance value of the insulation resistor and the estimated capacitance value of the Y capacitor, comprises:

[0036] constructing the state space model and the observation model;

[0037] The state quantity and state quantity error of the state space model are predicted by using the numerical integration method of the volume principle to obtain the state quantity prediction result, and the observation quantity and the observation quantity error of the observation model are predicted by using the state quantity prediction result to obtain the observation quantity prediction result;

[0038] The state quantity prediction result is corrected using the observed quantity prediction result to obtain a corrected state quantity prediction result, and the resistance estimation value of the insulation resistor and the capacitance estimation value of the Y capacitor are determined according to the corrected state quantity prediction result.

[0039] In some embodiments, the electric vehicle high-voltage system includes a power battery, a positive electrode insulation resistor, a negative electrode insulation resistor, a first Y capacitor, a second Y capacitor, and an electric chassis, wherein the positive electrode insulation resistor and the first Y capacitor are respectively connected between the positive electrode of the power battery and the electric chassis, and the negative electrode insulation resistor and the second Y capacitor are respectively connected between the negative electrode of the power battery and the electric chassis;

[0040] The insulation detection system includes a first current limiting resistor, a second current limiting resistor, a first switch, a second switch, a first sampling resistor, a second sampling resistor, a first voltage dividing resistor and a second voltage dividing resistor. The first current limiting resistor and the first switch are connected in series and connected between the positive electrode of the power battery and the electric chassis. The second current limiting resistor and the second switch are connected in series and connected between the negative electrode of the power battery and the electric chassis. The first voltage dividing resistor and the first sampling resistor are connected in series and connected in parallel with the second current limiting resistor and the second switch. The second voltage dividing resistor and the second sampling resistor are connected in series and connected between the positive electrode of the power battery and the negative electrode of the power battery.

[0041] The present application also provides an electric vehicle high voltage system detection device, comprising:

[0042] The first module is used to obtain voltage sampling data obtained by sampling the insulation detection system; the voltage sampling data is voltage waveform data obtained by the insulation detection system performing periodic voltage sampling on the electric vehicle high voltage system using an unbalanced bridge method, and the electric vehicle high voltage system has an insulation resistance and a Y capacitor;

[0043] The second module is used to perform voltage waveform classification processing on the voltage sampling data to obtain a classification result;

[0044] A third module is used to perform adaptive identification processing on the voltage sampling data using a nonlinear least squares estimation algorithm according to the classification result to obtain an identification result; the identification result includes response steady-state component information and response time information of the voltage sampling data;

[0045] The fourth module is used to use the identification result as the observation quantity to perform state quantity estimation processing on the state space model with the resistance value of the insulation resistor and the capacitance value of the Y capacitor as state quantities, so as to obtain the resistance estimation value of the insulation resistor and the capacitance estimation value of the Y capacitor.

[0046] An embodiment of the present application further provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned electric vehicle high-voltage system detection method when executing the computer program.

[0047] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned electric vehicle high-voltage system detection method is implemented.

[0048] The beneficial effects of the present application are as follows: the voltage sampling of the high-voltage system of an electric vehicle having an insulation resistor and a Y capacitor is performed periodically by using an unbalanced bridge method, the voltage waveform classification processing is performed on the voltage sampling data obtained by sampling, and the nonlinear least squares estimation algorithm is used according to the classification result to perform adaptive identification processing on the voltage sampling data, and then the state quantity estimation processing is performed on the state space model with the insulation resistor value and the Y capacitor value as the state quantity using the identification result as the observation quantity, so as to obtain the estimated values ​​of both the insulation resistance value and the Y capacitor. Since the voltage waveform classification processing is performed on the voltage sampling data first, and then the adaptive identification processing is performed on the voltage sampling data, when an insulation fault occurs in the high-voltage system of the electric vehicle, the insulation resistance value and the equivalent Y capacitor value can be quickly and accurately calculated, and the detection accuracy, response speed and robustness of the insulation resistance value can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for detecting a high voltage system of an electric vehicle provided in an embodiment of the present application.

[0050] Figure 2 It is a flowchart of the specific method of step S102 provided in an embodiment of the present application.

[0051] Figure 3 It is a flowchart of the specific method of step S103 provided in an embodiment of the present application.

[0052] Figure 4 It is a flowchart of the specific method of step S302 provided in an embodiment of the present application.

[0053] Figure 5 It is a flowchart of the specific method of step S104 provided in an embodiment of the present application.

[0054] Figure 6 It is a structural schematic diagram of an electric vehicle high voltage system detection device provided in an embodiment of the present application.

[0055] Figure 7 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application.

[0056] Figure 8 It is a structural schematic diagram of the electric vehicle high voltage system and insulation detection system provided in the embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0060] In the related art, passive and active methods are mainly used to detect the insulation resistance of electric vehicles. The passive method is mainly the bridge method, which forms an unbalanced bridge with the measured resistor and the known resistor, measures the voltage division on the measured resistor by changing the size of the current limiting resistor, and finally calculates the insulation resistance value by solving the equation. The active method is mainly the signal injection method, and its basic principle is to inject a detection signal into the high-voltage system through the vehicle's electric chassis, and then measure the feedback voltage signal on the sampling resistor to calculate the insulation resistance value.

[0061] In practical applications, the bridge method is widely used in electric vehicle insulation detection. However, the inventors found that due to the presence of Y capacitors between the two poles of the power battery and the electric chassis, a too short signal period will cause the sampling voltage to fail to reach a stable value, making the measurement result of the insulation resistance value smaller, causing the system to falsely alarm, and a too long signal period will reduce the response speed of the measurement system, and enhance the influence of the power battery voltage fluctuation on the measurement result, reducing the measurement accuracy. Therefore, for the detection of the insulation resistance of electric vehicles, it is necessary to take into account the influence of the Y capacitor, select a suitable signal period, and take corresponding compensation measures to improve the accuracy and stability of the measurement. At the same time, it is necessary to strengthen the protection against electromagnetic interference, improve the insulation performance between the positive and negative busbars of the power battery and the vehicle's electric chassis, and ensure the safe driving of electric vehicles.

[0062] Based on this, the embodiment of the present application provides an electric vehicle high-voltage system detection method and electronic equipment, which is based on the unbalanced bridge method, combined with an adaptive nonlinear least squares estimation algorithm and a filtering algorithm to achieve the measurement of insulation resistance and equivalent Y capacitance, thereby improving the detection accuracy of insulation resistance.

[0063] See also Figure 1 , Figure 1 is a flow chart of a method for detecting a high voltage system of an electric vehicle provided in an embodiment of the present application. In some embodiments of the present application, Figure 1The method may specifically include but is not limited to steps S101 to S104. Figure 1 These four steps are introduced in detail.

[0064] Step S101, obtaining voltage sampling data sampled by an insulation detection system.

[0065] Among them, the voltage sampling data is the voltage waveform data obtained by the insulation detection system using the unbalanced bridge method to periodically sample the voltage of the electric vehicle high-voltage system. The electric vehicle high-voltage system has an insulation resistor and a Y capacitor. Specifically, the insulation resistor and the Y capacitor are both arranged in the high-voltage part of the electric vehicle high-voltage system. The insulation resistor and the Y capacitor are connected between the positive electrode of the power battery and the electric chassis or between the negative electrode of the power battery and the electric chassis. The insulation detection system performs periodic voltage sampling on the electric vehicle high-voltage system based on the unbalanced bridge method, and obtains the voltage sampling data after multiple sampling cycles.

[0066] Step S102, performing voltage waveform classification processing on the voltage sampling data to obtain a classification result.

[0067] Since the high-voltage system of the electric vehicle has a Y capacitor, the voltage sampling data sampled by the insulation detection system is a periodically fluctuating exponential function wave. The voltage waveforms of the voltage sampling data in the first half cycle and the second half cycle of the sampling period are respectively exponential function waveforms that change with time. When the resistance value of the insulation resistance decreases, the voltage waveform of the voltage sampling data will evolve into a square wave. During the evolution process, a critical square wave will appear. The voltage waveforms of the voltage sampling data in the first half cycle and the second half cycle of the sampling period are respectively critical square waves that change with time. When the resistance value of the insulation resistance is further reduced, the voltage waveforms of the voltage sampling data in the first half cycle and the second half cycle of the sampling period are respectively square waves that change with time.

[0068] When the voltage sampling data is an exponential function wave, the parameter identification method can be directly used to determine the voltage value vector of the voltage sampling data. The voltage value vector includes the response steady-state component, response gain component and response time constant of the voltage sampling data. When the voltage sampling data is a critical square wave or a square wave, directly using the parameter identification method will result in non-convergence, and it is impossible to accurately obtain the identification result under this fault mode.

[0069] In step S102, the voltage waveform classification processing is performed on the voltage sampling data, specifically, a number of sampling cycles are selected from the voltage sampling data, the voltage sampling data corresponding to the first half cycle or the second half cycle in the sampling cycle is used as the sample data, the waveform slope of the sample data is calculated, and the waveform type of the voltage sampling data is determined according to the calculated value of the waveform slope, and the classification result of the voltage sampling data being an exponential function wave, a critical square wave or a square wave is obtained. Specifically, the waveform slope of the sample data can be calculated by selecting a number of local areas from the sample data, respectively calculating the voltage average value of each local area, and then the waveform slope of the sample data can be calculated based on the calculated voltage average value.

[0070] like Figure 2 As shown, in some embodiments, step S102 may specifically include but is not limited to steps S201 to S202. Figure 2 These two steps are introduced in detail.

[0071] Step S201 , performing waveform slope estimation processing on the voltage sampling data to obtain a slope estimation value.

[0072] Step S202, when the slope estimation value is in the first slope interval, the voltage sampling data is classified as an exponential function wave, when the slope estimation value is in the second slope interval, the voltage sampling data is classified as a critical square wave, and when the slope estimation value is in the third slope interval, the voltage sampling data is classified as a square wave.

[0073] Among them, the critical values ​​of the first slope interval, the second slope interval and the third slope interval increase in sequence.

[0074] In steps S201 to S202, the voltage sampling data corresponding to the first half cycle or the second half cycle in the sampling period is used as the sample data, and the voltage average values ​​of the initial stage, the middle stage and the final stage of the sample data are calculated respectively, and then the average value of the ratio between the three groups of voltage average values ​​is calculated to obtain the slope estimation value of the sample data, determine the slope interval in which the slope estimation value is located, and obtain the classification result.

[0075] For example, the cycle length of the first half cycle or the second half cycle is 0.75s, the sampling interval is 0.001s, and the selected sample data has 750 voltage sampling values. It is set that when the steady-state value is reached in more than 2% of the time, the voltage sampling data is considered to be an exponential function wave, when the steady-state value is reached in 1%-2% of the time, the voltage sampling data is considered to be a critical square wave, and when the steady-state value is reached in less than 1% of the time, the voltage sampling data is considered to be a square wave. The starting stage is the 10th to 60th voltage sampling values, the middle stage is the 375th to 425th voltage sampling values, and the end stage is the 700th to 750th voltage sampling values. Sampling value, calculate the voltage average values ​​of the initial stage, middle stage and final stage of the sample data respectively, obtain the first voltage average value, the second voltage average value and the third voltage average value, then calculate the ratio of the second voltage average value to the first voltage average value, the ratio of the third voltage average value to the first voltage average value and the ratio of the third voltage average value to the second voltage average value, and then calculate the average value of the three ratios to obtain the slope estimation value of the sample data, determine the slope interval where the slope estimation value is located, thereby determining whether the waveform of the current voltage sampling data is an exponential function wave, a critical square wave or a square wave.

[0076] Step S103 , according to the classification result, a nonlinear least square estimation algorithm is used to perform adaptive identification processing on the voltage sampling data to obtain an identification result.

[0077] The identification result includes response steady-state component information and response time information of the voltage sampling data.

[0078] In step S103, according to the specific classification result, a function expression of the corresponding voltage sampling data is constructed, and then a parameter identification method is used to determine the voltage value vector of the voltage sampling data. The identification process of determining the voltage value vector of the voltage sampling data is converted into a nonlinear unconstrained least squares optimization problem. The voltage value vector of the voltage sampling data is iteratively generated through a nonlinear least squares estimation algorithm, and then it is verified whether the generated voltage value vector meets the preset iteration conditions. When the generated voltage value vector meets the preset iteration conditions and the algorithm meets the preset convergence conditions, the algorithm iteration is terminated, and the last voltage value vector that meets the iteration conditions is output. The voltage value vector response steady-state component and the response time constant are combined as the identification result.

[0079] like Figure 3 As shown, in some embodiments, step S103 may specifically include but is not limited to steps S301 to S303. Figure 3 These three steps are introduced in detail.

[0080] Step S301, constructing a difference function according to the classification result.

[0081] Among them, the difference function is fitted by an exponential function vector and a voltage sampling value, the exponential function vector has a voltage value vector, when the voltage sampling data is an exponential function wave, the voltage value vector is composed of a preset first response steady-state component, a first response gain component and a first response time constant, when the voltage sampling data is a critical square wave, the voltage value vector is composed of a second response steady-state component, a second response gain component and a second response time constant, when the voltage sampling data is a square wave, the voltage value vector is composed of a second response steady-state component, the second response steady-state component is generated by the average voltage value at the end stage of the current half sampling period, the second response gain component is generated by a preset constant, and the second response time constant is fitted by the structural parameters of the second response steady-state component, the electric vehicle high-voltage system and the insulation detection system.

[0082] It can be understood that the difference function describes the waveform of the first half cycle or the second half cycle of each sampling cycle of the voltage sampling data. The waveform of the first half cycle or the second half cycle is fitted by different voltage value vectors and voltage sampling values ​​respectively. Therefore, the voltage sampling data corresponds to two difference functions with different voltage value vectors (response steady-state component, response gain component and / or response time constant). The response steady-state component is the stable value of the sampling voltage and is also the sampling voltage value when there is no Y capacitor. The response gain is the difference between the sampling voltage value when the insulation detection system is connected and the sampling voltage value when it is stable. The response time constant is the time constant of the equivalent Y capacitor.

[0083] Specifically, when the voltage sampling data is an exponential function wave, the expression of the difference function is:

[0084] ,

[0085] When the voltage sampling data is a critical square wave, the expression of the difference function is:

[0086] ,

[0087] When the voltage sampling data is a square wave, the expression of the difference function is:

[0088] ,

[0089] in, is the ith difference function of the current half sampling period, is the i-th exponential function vector of the current half sampling period, is the first response steady-state component of the current half sampling period, is the first response gain component of the current half sampling period, is the first response time constant of the current half sampling period, is the i-th sampling moment of the current half sampling period, is the i-th voltage sampling value of the current half sampling period, is the second response steady-state component of the current half sampling period, is the second response gain component of the current half sampling period, is the second response time constant of the current half sampling period.

[0090] The process of constructing the difference function is described below in conjunction with a specific embodiment.

[0091] like Figure 8 As shown, in some embodiments, the high-voltage system of the electric vehicle includes a power battery, a positive electrode insulation resistor Rp, a negative electrode insulation resistor Rn, a first Y capacitor Cp, a second Y capacitor Cn and an electric chassis, and the insulation detection system includes a first current limiting resistor R1, a second current limiting resistor R2, a first switch S1, a second switch S2, a first voltage dividing resistor R4, a first sampling resistor R5, a second voltage dividing resistor R3 and a second sampling resistor R6, and the resistance value of the first current limiting resistor R1 is equal to the resistance value of the second current limiting resistor R2. Among them, the positive electrode insulation resistor Rp and the first Y capacitor Cp are respectively connected between the positive electrode of the power battery and the electric chassis, the negative electrode insulation resistor Rn and the second Y capacitor Cn are respectively connected between the negative electrode of the power battery and the electric chassis, the first current limiting resistor R1 and the first switch S1 are connected in series and then connected between the positive electrode of the power battery and the electric chassis, the second current limiting resistor R2 and the second switch S2 are connected in series and then connected between the negative electrode of the power battery and the electric chassis, the first voltage dividing resistor R4 and the first sampling resistor R5 are connected in series and then connected in parallel with the second current limiting resistor R2 and the second switch S2, and the second voltage dividing resistor R3 and the second sampling resistor R6 are connected in series and then connected between the positive electrode of the power battery and the negative electrode of the power battery.

[0092] When the voltage sampling data is an exponential function wave, the process of generating the first response steady-state component, the first response gain component and the first response time constant according to the preset generation rule is as follows:

[0093] ,

[0094] ,

[0095] When the voltage sampling data is a critical square wave, the exponential component in the voltage sampling data is greatly reduced, so the second response gain component is much smaller than the second response steady-state component. The process of generating the second response steady-state component, the second response gain component and the second response time constant is as follows:

[0096] ,

[0097] ,

[0098] ,

[0099] ,

[0100] ,

[0101] ,

[0102] When the voltage sampling data is a square wave, the process of generating the second response steady-state component is as follows:

[0103] ,

[0104] ,

[0105] in, is the second response steady-state component of the neighbor half sampling period, is the second response gain component of the neighbor half sampling period, is the second response time constant of the neighbor half sampling period, is the voltage sampling mean value in the current half sampling period, is the average voltage sampling value within the neighbor half sampling period, is the sth voltage sampling value of the current half sampling period, is the sth voltage sampling value of the neighbor half sampling period, is a constant of magnitude, and are all calculation factors.

[0106] It can be understood that the current half sampling period and the neighbor half sampling period constitute a sampling period. In the above insulation detection system, when the first switch S1 is closed, the second switch S2 is disconnected, so that the second voltage-dividing resistor R3 is connected to the high-voltage system of the electric vehicle. When the first switch S1 is disconnected, the second switch S2 is closed, so that the second sampling resistor R6 is connected to the high-voltage system of the electric vehicle. This cycle is repeated to form voltage sampling data with multiple sampling periods. Each sampling period consists of a first half sampling period and a second half sampling period. When the first half sampling period is the current half sampling period, the second half sampling period is the neighbor half sampling period. Conversely, when the second half sampling period is the current half sampling period, the first half sampling period is the neighbor half sampling period.

[0107] Step S302, adopting a nonlinear least square estimation algorithm to iteratively update the voltage value vector, so that the error of the difference function is iteratively reduced until it meets a preset convergence condition, and obtains an iterated voltage value vector.

[0108] like Figure 4 As shown, in some embodiments, step S302 may specifically include but is not limited to steps S401 to S409. Figure 4 and Figure 8 These nine steps are introduced in detail.

[0109] Step S401, generating a voltage value vector of the current iteration cycle.

[0110] In step S401, the voltage value vector of the constructed difference function is used as the initial voltage value vector. In the first iteration cycle, the initial voltage value vector is used as the voltage value vector of the current iteration cycle. In non-first iteration cycles, the voltage value vector corrected in the previous iteration cycle is used as the voltage value vector of the current iteration cycle. At the same time, the correction coefficient, the growth factor (the value of the growth factor is set to be greater than 1) and the termination precision value are set.

[0111] Step S402, calculating the current error of the difference function according to the voltage value vector of the current iteration cycle, and constructing a Jacobian matrix and a quasi-Hessian matrix.

[0112] In step S402, the formula for calculating the current error of the difference function is:

[0113] ,

[0114] The formula for constructing the Jacobian matrix is:

[0115] ,

[0116] The formula for constructing the quasi-Hessian matrix is:

[0117] ,

[0118] in, is the voltage value vector of the kth iteration cycle, is the ith difference function of the current half sampling period of the kth iteration period, is the current error of the difference function in the kth iteration period, The Jacobian matrix constructed for the kth iteration period, is the quasi-Hessian matrix constructed for the kth iteration period, for The transposed matrix of , n is the total number of voltage sampling values ​​in the current half sampling period.

[0119] Step S403, calculating a correction amount according to the Jacobian matrix and the quasi-Hessian matrix, and using the correction amount to correct the voltage value vector of the current iteration cycle to obtain a corrected difference function.

[0120] In step S403, the formula for calculating the correction amount is:

[0121] ,

[0122] The formula for calculating the corrected difference function is:

[0123] ,

[0124] ,

[0125] in, is the correction amount of the kth iteration cycle, is the correction coefficient of the kth iteration cycle, I is the unit matrix, is the corrected voltage value vector of the kth iteration cycle, is the difference function vector of the kth iteration cycle, .

[0126] Step S404: Calculate the corrected error of the difference function according to the corrected difference function.

[0127] Step S405, determine whether the corrected error is smaller than the current error. If not, execute step S406; if smaller than the current error, execute step S407.

[0128] Step S406, adjusting the correction coefficient. Return to step S403.

[0129] In step S406, the formula for adjusting the correction coefficient is:

[0130] ,

[0131] in, is the modified correction coefficient of the kth iteration cycle, is the growth factor.

[0132] Step S407, determine whether the norm of the difference between the voltage value vector corrected in the current iteration cycle and the voltage value vector corrected in the previous iteration cycle is less than the preset termination accuracy value. If it is less than the termination accuracy value, execute step S408; if it is not less than the termination accuracy value, execute step S409.

[0133] Step S408: taking the voltage value vector of the current iteration cycle as the voltage value vector after iteration.

[0134] Step S409, adjust the correction coefficient, and use the next iteration cycle as the current iteration cycle. Return to step S401.

[0135] In step S409, the formula for adjusting the correction coefficient is:

[0136] ,

[0137] After adjusting the correction coefficient, the voltage value vector corrected in the current iteration cycle is used as the voltage value vector of the next iteration cycle, and then the current iteration cycle is used as the previous iteration cycle, and the next iteration cycle is used as the current iteration cycle, and the above iteration steps are repeated until the above convergence conditions are met.

[0138] Step S303: construct a measurement matrix using the iterated voltage value vector as an identification result.

[0139] In step S303, after obtaining the iterated voltage value vector, the iterated voltage value vector is used to construct a measurement matrix. When the voltage sampling data is an exponential function wave, the measurement matrix is:

[0140] ,

[0141] When the voltage sampling data is a critical square wave, the measurement matrix is:

[0142] ,

[0143] When the voltage sampling data is a square wave, the measurement matrix is:

[0144] ,

[0145] Where Z is the measurement matrix.

[0146] Step S104, using the identification result as the observed quantity, performing state quantity estimation processing on the state space model using the insulation resistance value and the Y capacitor value as the state quantity, to obtain the insulation resistance estimation value and the Y capacitor capacitance estimation value.

[0147] like Figure 5 As shown, in some embodiments, step S104 may specifically include but is not limited to steps S501 to S503. Figure 5 and Figure 8 These three steps are introduced in detail.

[0148] Step S501, constructing a state space model and an observation model.

[0149] In step S501, the state space model is constructed with the insulation resistance and the capacitance of the Y capacitor as state quantities, and the observation model is constructed with the identification result as the observation quantity. The state space model and the observation model constitute a discrete system, and the expression of the discrete system is:

[0150] ,

[0151] ,

[0152] in, is the system state vector at the mth observation moment, is the system transfer function at the m-1th observation time, is the process noise at the m-1th observation moment, is the resistance value of the positive electrode insulation resistance at the mth observation moment, is the estimated value of the negative electrode insulation resistance at the mth observation moment, is the estimated capacitance value of capacitor Y at the mth observation moment, is the system observation vector at the mth observation moment, is the system observation function at the m-1th observation moment, is the measurement noise at the m-1th observation moment, is the transposed matrix of the measurement matrix at the mth observation moment.

[0153] According to the above discrete system, the expression of the system observation function can be obtained as follows:

[0154] ,

[0155] in, is the system observation function at the mth observation moment.

[0156] Step S502, using the numerical integration method of the volume principle to predict the state quantity and state quantity error of the state space model to obtain the state quantity prediction result, and using the state quantity prediction result to predict the observation quantity and observation quantity error of the observation model to obtain the observation quantity prediction result.

[0157] The process of step S502 is described in detail below.

[0158] Initialize the state vector and observation vector:

[0159] ,

[0160] ,

[0161] ,

[0162] in, is the system initial state vector, is the system initial error covariance matrix, is the square root of the system initial error covariance matrix, is the initial resistance estimate of the positive electrode insulation resistance, is the initial resistance estimate of the negative electrode insulation resistance, is the estimated initial capacitance of the Y capacitor.

[0163] Compute the volume point of the state vector:

[0164] ,

[0165] in, is the volume point array at the mth observation time The jth column of is the jth volume point, is the square root of the system error covariance matrix at the mth observation moment, is the estimated value of the system state at the mth observation moment.

[0166] After propagating the volume points of the insulation detection system state quantity and weighting the propagated volume points, the predicted value of the insulation detection system state quantity is estimated, and the square root of the error covariance matrix is ​​calculated:

[0167] ,

[0168] ,

[0169] ,

[0170] ,

[0171] ,

[0172] in, To use the volume point value at the mth observation time to predict the volume point array at the m+1th observation time The jth column of is the predicted value of the system state vector at the m+1th observation time, and t is The number of parameters, is the square root of the system error covariance matrix at the m+1th observation moment, To perform QR decomposition on the matrix, for and The deviation, is the square root of the error covariance matrix of the motion model noise in the prediction process, and Q is the system process noise covariance matrix.

[0173] According to the predicted value of the system state quantity and the square root of its error covariance matrix, a set of equally weighted volume points are updated, and the system observation function is used for observation to calculate the predicted value of the observed quantity and the square root of its covariance matrix:

[0174] ,

[0175] ,

[0176] ,

[0177] ,

[0178] ,

[0179] ,

[0180] in, is the volume point array at the m+1th observation time after update The jth column of is the observation vector at the m+1th observation moment The jth column of is the predicted value of the system observation vector at the m+1th observation time, for and The deviation is the square root of the error covariance matrix of the measurement noise in the prediction process, and R is the covariance matrix of the measurement noise.

[0181] Calculate the square root of the cross-covariance matrix of the measured and predicted values, and then calculate the Kalman gain. According to the Kalman gain, calculate the best estimate of the system state vector at the m+1th observation time:

[0182] ,

[0183] ,

[0184] ,

[0185] ,

[0186] in, is the square root of the cross-covariance matrix between the state vector and the observation vector at the m+1th observation moment, for and The deviation is the Kalman gain of the state at the m+1th observation moment, is the best predicted value of the system state vector at the m+1th observation time, is the observation vector at the m+1th observation moment.

[0187] Step S503, using the observed quantity prediction result to correct the state quantity prediction result to obtain a corrected state quantity prediction result, and determining the resistance estimation value of the insulation resistance and the capacitance estimation value of the Y capacitor according to the corrected state quantity prediction result.

[0188] In step S503, the optimal predicted value of the system state vector obtained in the above steps is used as the revised state quantity prediction result. The revised state quantity prediction result includes the resistance estimation value information of the insulation resistor and the capacitance estimation value information of the Y capacitor. By extracting information from the revised state quantity prediction result, the resistance estimation value of the insulation resistor and the capacitance estimation value of the Y capacitor can be obtained.

[0189] See also Figure 6 The embodiment of the present application also provides an electric vehicle high voltage system detection device, which can implement the above electric vehicle high voltage system detection method, and the device includes:

[0190] The first module 601 is used to obtain voltage sampling data obtained by sampling the insulation detection system; the voltage sampling data is voltage waveform data obtained by the insulation detection system performing periodic voltage sampling on the high-voltage system of the electric vehicle using an unbalanced bridge method, and the high-voltage system of the electric vehicle has insulation resistance and Y capacitance;

[0191] The second module 602 is used to perform voltage waveform classification processing on the voltage sampling data to obtain a classification result;

[0192] The third module 603 is used to perform adaptive identification processing on the voltage sampling data using a nonlinear least square estimation algorithm according to the classification result to obtain an identification result; the identification result includes response steady-state component information and response time information of the voltage sampling data;

[0193] The fourth module 604 is used to use the identification result as the observation quantity to perform state quantity estimation processing on the state space model with the insulation resistance and the Y capacitor as the state quantity, so as to obtain the estimated resistance value of the insulation resistance and the estimated capacitance value of the Y capacitor.

[0194] The specific implementation of the electric vehicle high voltage system detection device is basically the same as the specific implementation of the electric vehicle high voltage system detection method described above, and will not be described in detail here.

[0195] Figure 7 It is a block diagram of an electronic device according to an exemplary embodiment.

[0196] Refer to the following Figure 7 An electronic device 700 according to this embodiment of the present disclosure is described. Figure 7 The electronic device 700 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0197] like Figure 7As shown, the electronic device 700 is in the form of a general computing device. The components of the electronic device 700 may include, but are not limited to: at least one processing unit 710, at least one storage unit 720, a bus 730 connecting different system components (including the storage unit 720 and the processing unit 710), a display unit 740, etc.

[0198] The storage unit stores program codes, which can be executed by the processing unit 710, so that the processing unit 710 executes the steps according to various exemplary embodiments of the present disclosure described in the above electric vehicle high-voltage system detection method section of this specification.

[0199] The storage unit 720 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 7201 and / or a cache storage unit 7202 , and may further include a read-only storage unit (ROM) 7203 .

[0200] The storage unit 720 may also include a program / utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0201] Bus 730 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0202] The electronic device 700 may also communicate with one or more external devices 700' (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 700, and / or communicate with any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 750. Furthermore, the electronic device 700 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 760. The network adapter 760 may communicate with other modules of the electronic device 700 via a bus 730. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0203] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned electric vehicle high-voltage system detection method is implemented.

[0204] The electric vehicle high-voltage system detection method and electronic device provided in the embodiment of the present application use the unbalanced bridge method to perform periodic voltage sampling on the electric vehicle high-voltage system having insulation resistance and Y capacitor, perform voltage waveform classification processing on the sampled voltage sampling data, and use the nonlinear least squares estimation algorithm according to the classification result to perform adaptive identification processing on the voltage sampling data, and then use the identification result as the observation quantity to perform state quantity estimation processing on the state space model with the insulation resistance value and the Y capacitor value as the state quantity, and obtain the estimated values ​​of both the insulation resistance value and the Y capacitor. Since the voltage waveform classification processing is first performed on the voltage sampling data, and then the adaptive identification processing is performed on the voltage sampling data, when an insulation fault occurs in the electric vehicle high-voltage system, the insulation resistance value and the equivalent Y capacitor value can be quickly and accurately calculated, and the detection accuracy, response speed and robustness of the insulation resistance value can be effectively improved.

[0205] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the implementation of the present disclosure.

[0206] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0207] Computer readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program codes contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0208] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0209] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be changed accordingly and only used in one or more devices different from the embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.

[0210] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0211] The exemplary embodiments of the present disclosure are specifically shown and described above. It should be understood that the present disclosure is not limited to the detailed structures, configurations or implementations described herein; on the contrary, the present disclosure is intended to cover various modifications and equivalent configurations included in the spirit and scope of the appended claims.

Claims

1. A method for detecting a high voltage system of an electric vehicle, characterized in that: include: Obtain voltage sampling data obtained by sampling the insulation detection system; The voltage sampling data is voltage waveform data obtained by the insulation detection system performing periodic voltage sampling on the electric vehicle high voltage system using an unbalanced bridge method, and the electric vehicle high voltage system has an insulation resistance and a Y capacitor; Performing voltage waveform classification processing on the voltage sampling data to obtain a classification result; According to the classification result, a nonlinear least squares estimation algorithm is used to perform adaptive identification processing on the voltage sampling data to obtain an identification result; The identification result includes response steady-state component information and response time information of the voltage sampling data; Taking the identification result as an observation quantity, performing state quantity estimation processing on a state space model taking the resistance value of the insulation resistor and the capacitance value of the Y capacitor as state quantities, to obtain a resistance estimation value of the insulation resistor and a capacitance estimation value of the Y capacitor; The performing voltage waveform classification processing on the voltage sampling data to obtain a classification result includes: Performing waveform slope estimation processing on the voltage sampling data to obtain a slope estimation value; When the slope estimation value is in a first slope interval, a classification result of the voltage sampling data being an exponential function wave is obtained; when the slope estimation value is in a second slope interval, a classification result of the voltage sampling data being a critical square wave is obtained; when the slope estimation value is in a third slope interval, a classification result of the voltage sampling data being a square wave is obtained; the critical values ​​of the first slope interval, the second slope interval and the third slope interval are increased in sequence; The method of performing adaptive identification processing on the voltage sampling data using a nonlinear least squares estimation algorithm according to the classification result to obtain an identification result includes: According to the classification result, a difference function is constructed; the difference function is fitted by an exponential function vector and a voltage sampling value, the exponential function vector has a voltage value vector, when the voltage sampling data is an exponential function wave, the voltage value vector is composed of a preset first response steady-state component, a first response gain component and a first response time constant, when the voltage sampling data is a critical square wave, the voltage value vector is composed of a second response steady-state component, a second response gain component and a second response time constant, when the voltage sampling data is a square wave, the voltage value vector is composed of a second response steady-state component, the second response steady-state component is generated by an average voltage value at the end stage of the current half sampling period, the second response gain component is generated by a preset constant, and the second response time constant is fitted by the second response steady-state component, the structural parameters of the electric vehicle high-voltage system and the insulation detection system; Adopting a nonlinear least squares estimation algorithm to iteratively update the voltage value vector, so that the error of the difference function is iteratively reduced until a preset convergence condition is met, thereby obtaining an iterated voltage value vector; constructing a measurement matrix using the iterated voltage value vector as the identification result; When the voltage sampling data is an exponential function wave, the expression of the difference function is: ， When the voltage sampling data is a critical square wave, the expression of the difference function is: ， When the voltage sampling data is a square wave, the expression of the difference function is: ， in, is the ith difference function of the current half sampling period, is the i-th exponential function vector of the current half sampling period, is the first response steady-state component of the current half sampling period, is the first response gain component of the current half sampling period, is the first response time constant of the current half sampling period, is the i-th sampling moment of the current half sampling period, is the i-th voltage sampling value of the current half sampling period, is the second response steady-state component of the current half sampling period, is the second response gain component of the current half sampling period, is the second response time constant of the current half sampling period.

2. The electric vehicle high voltage system detection method according to claim 1, characterized in that: The nonlinear least square estimation algorithm is adopted to iteratively update the voltage value vector so that the error of the difference function is iteratively decreased until a preset convergence condition is met to obtain the iterated voltage value vector, including: Generate a voltage value vector for the current iteration cycle; According to the voltage value vector of the current iteration cycle, the current error of the difference function is calculated, and a Jacobian matrix and a quasi-Hessian matrix are constructed; Calculating a correction amount according to the Jacobian matrix and the quasi-Hessian matrix, and using the correction amount to correct the voltage value vector of the current iteration cycle to obtain a corrected difference function; Calculating a corrected error of the difference function according to the corrected difference function; Determining whether the corrected error is less than the current error; If it is not less than the current error, adjusting the correction coefficient; returning to the step of calculating the correction amount according to the Jacobian matrix and the quasi-Hessian matrix, and using the correction amount to correct the voltage value vector of the current iteration cycle to obtain a corrected voltage value vector; If it is smaller than the current error, determining whether the norm of the difference between the voltage value vector corrected in the current iteration cycle and the voltage value vector corrected in the previous iteration cycle is smaller than a preset termination accuracy value; If it is less than the termination accuracy value, the voltage value vector of the current iteration cycle is used as the voltage value vector after the iteration; If it is not less than the termination accuracy value, adjust the correction coefficient and use the next iteration cycle as the current iteration cycle; return to the step of generating the voltage value vector of the current iteration cycle.

3. The electric vehicle high voltage system detection method according to claim 1, characterized in that: The method of using the identification result as an observation quantity and performing state quantity estimation processing on a state space model using the resistance value of the insulation resistor and the capacitance value of the Y capacitor as state quantities to obtain a resistance estimation value of the insulation resistor and a capacitance estimation value of the Y capacitor includes: constructing the state space model and the observation model; The state quantity and state quantity error of the state space model are predicted by using the numerical integration method of the volume principle to obtain the state quantity prediction result, and the observation quantity and the observation quantity error of the observation model are predicted by using the state quantity prediction result to obtain the observation quantity prediction result; The state quantity prediction result is corrected using the observed quantity prediction result to obtain a corrected state quantity prediction result, and the resistance estimation value of the insulation resistor and the capacitance estimation value of the Y capacitor are determined according to the corrected state quantity prediction result.

4. The electric vehicle high voltage system detection method according to any one of claims 1 to 3, characterized in that: The electric vehicle high-voltage system includes a power battery, a positive electrode insulation resistor, a negative electrode insulation resistor, a first Y capacitor, a second Y capacitor and an electric chassis, wherein the positive electrode insulation resistor and the first Y capacitor are respectively connected between the positive electrode of the power battery and the electric chassis, and the negative electrode insulation resistor and the second Y capacitor are respectively connected between the negative electrode of the power battery and the electric chassis; The insulation detection system includes a first current limiting resistor, a second current limiting resistor, a first switch, a second switch, a first sampling resistor, a second sampling resistor, a first voltage dividing resistor and a second voltage dividing resistor. The first current limiting resistor and the first switch are connected in series and connected between the positive electrode of the power battery and the electric chassis. The second current limiting resistor and the second switch are connected in series and connected between the negative electrode of the power battery and the electric chassis. The first voltage dividing resistor and the first sampling resistor are connected in series and connected in parallel with the second current limiting resistor and the second switch. The second voltage dividing resistor and the second sampling resistor are connected in series and connected between the positive electrode of the power battery and the negative electrode of the power battery.

5. A high voltage system detection device for electric vehicles, characterized in that: include: The first module is used to obtain voltage sampling data obtained by sampling of the insulation detection system; The voltage sampling data is voltage waveform data obtained by the insulation detection system performing periodic voltage sampling on the electric vehicle high voltage system using an unbalanced bridge method, and the electric vehicle high voltage system has an insulation resistance and a Y capacitor; The second module is used to perform voltage waveform classification processing on the voltage sampling data to obtain a classification result; The third module is used to perform adaptive identification processing on the voltage sampling data according to the classification result by using a nonlinear least squares estimation algorithm to obtain an identification result; The identification result includes response steady-state component information and response time information of the voltage sampling data; A fourth module is used to use the identification result as an observation quantity to perform state quantity estimation processing on a state space model with the resistance value of the insulation resistor and the capacitance value of the Y capacitor as state quantities, so as to obtain a resistance estimation value of the insulation resistor and a capacitance estimation value of the Y capacitor; The performing voltage waveform classification processing on the voltage sampling data to obtain a classification result includes: Performing waveform slope estimation processing on the voltage sampling data to obtain a slope estimation value; When the slope estimation value is in a first slope interval, a classification result of the voltage sampling data being an exponential function wave is obtained; when the slope estimation value is in a second slope interval, a classification result of the voltage sampling data being a critical square wave is obtained; when the slope estimation value is in a third slope interval, a classification result of the voltage sampling data being a square wave is obtained; the critical values ​​of the first slope interval, the second slope interval and the third slope interval are increased in sequence; The method of performing adaptive identification processing on the voltage sampling data using a nonlinear least squares estimation algorithm according to the classification result to obtain an identification result includes: According to the classification result, a difference function is constructed; the difference function is fitted by an exponential function vector and a voltage sampling value, the exponential function vector has a voltage value vector, when the voltage sampling data is an exponential function wave, the voltage value vector is composed of a preset first response steady-state component, a first response gain component and a first response time constant, when the voltage sampling data is a critical square wave, the voltage value vector is composed of a second response steady-state component, a second response gain component and a second response time constant, when the voltage sampling data is a square wave, the voltage value vector is composed of a second response steady-state component, the second response steady-state component is generated by an average voltage value at the end stage of the current half sampling period, the second response gain component is generated by a preset constant, and the second response time constant is fitted by the second response steady-state component, the structural parameters of the electric vehicle high-voltage system and the insulation detection system; Adopting a nonlinear least squares estimation algorithm to iteratively update the voltage value vector, so that the error of the difference function is iteratively reduced until a preset convergence condition is met, thereby obtaining an iterated voltage value vector; constructing a measurement matrix using the iterated voltage value vector as the identification result; When the voltage sampling data is an exponential function wave, the expression of the difference function is: ， When the voltage sampling data is a critical square wave, the expression of the difference function is: ， When the voltage sampling data is a square wave, the expression of the difference function is: ， in, is the ith difference function of the current half sampling period, is the i-th exponential function vector of the current half sampling period, is the first response steady-state component of the current half sampling period, is the first response gain component of the current half sampling period, is the first response time constant of the current half sampling period, is the i-th sampling moment of the current half sampling period, is the i-th voltage sampling value of the current half sampling period, is the second response steady-state component of the current half sampling period, is the second response gain component of the current half sampling period, is the second response time constant of the current half sampling period.

6. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the electric vehicle high-voltage system detection method according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the electric vehicle high voltage system detection method according to any one of claims 1 to 4 is implemented.

Images