Method and system for monitoring and identifying insulation resistance parameters of electric vehicle

Abstract

The invention discloses a method and a system for monitoring and identifying insulation resistance parameters of an electric vehicle. The method comprises the following steps: establishing a circuit model of an insulation resistance monitoring circuit of the electric vehicle; wherein the circuit model comprises a square wave injection source and a bridge arm branch used for importing a square wave signal, and a bridge arm resistor is connected to the bridge arm branch in series and used for capturing a monitoring signal; converting the model into an operational circuit model in a complex frequency domain for calculation by adopting Laplacian transformation, and establishing an algebraic equation of an incidence relation between the voltage of the bridge arm resistor and the insulation resistance of the electric vehicle; simultaneously applying a voltage in a linear change form of the power battery pack and a square wave voltage injected by a square wave source to the circuit model, carrying out voltage sampling, identifying circuit parameters through a nonlinear least square method based on an algebraic equation, and forming a target function by taking a minimum error quadratic sum of a sampling value and a calculation value as a target; and obtaining a circuit parameter optimal solution enabling the objective function to be minimum based on a Gaussian Newton iteration method so as to obtain a monitoring value of the insulation resistance.

Description

Technical Field

[0001] The present invention relates to the technical field of online insulation resistance monitoring, and more specifically, to a method and system for monitoring and identifying insulation resistance parameters of an electric vehicle. Background Art

[0002] As a clean energy means of transportation, electric vehicles are receiving more and more attention and promotion in more and more countries and regions. The safety of electric vehicles cannot be ignored. Electric vehicle fire accidents occur from time to time, which seriously threatens the personal safety of electric vehicle owners. Therefore, it is particularly important to monitor the insulation performance of electric vehicles.

[0003] During the use of electric vehicles, the insulation performance of its insulation materials is not static. For example, under the influence of thermal stress and electrical stress, the performance of the insulation materials in the high-voltage system may change, which in turn leads to a decrease in the insulation strength of the cable; chemical liquids may adhere to the surface of the insulation material, triggering chemical reactions such as oxidation and decomposition, thereby changing the chemical structure of the material and causing insulation aging; the vibration of electric vehicles may cause cable wear, and in low temperature environments, the insulation material may become hard and brittle, thereby changing its physical structure and causing insulation failure. Insulation failure will not only affect the operation of the electrical system, but more seriously, there will be a risk of electric shock to the human body; in order to ensure the safety of the lives of drivers and passengers, it is necessary to monitor the insulation of electric vehicles, that is, to monitor the insulation between the high-voltage electrical system of electric vehicles and the chassis of electric vehicles in real time.

[0004] At present, pure electric vehicles mainly use direct current to work. Generally speaking, the online monitoring of electric vehicle insulation resistance measures the insulation resistance of the positive and negative electrodes of the power battery pack to the chassis, and judges the insulation performance of the electric vehicle by the insulation resistance value. There are many methods for DC system insulation monitoring, including leakage current method, bridge method, injection method, etc., and the existing DC system insulation monitoring methods have more or less shortcomings. Therefore, the online monitoring method of electric vehicle insulation resistance still needs to be further improved and optimized.

[0005] At present, most electric vehicle insulation resistance monitoring uses the traditional injection method. However, when the total pressure of the vehicle fluctuates, the voltage across the sampling resistor may also be affected, resulting in inaccurate measurement results. At the same time, it is easily affected by the parasitic capacitance of the electric vehicle DC system and the safety Y capacitor (collectively referred to as Y capacitor). It is necessary to wait until the capacitor is fully charged and discharged before sampling. The cycle will be lengthened, otherwise the insulation resistance measurement result will be too small, causing the insulation monitoring device to falsely alarm. Therefore, the main problem of online monitoring of electric vehicle insulation resistance is how to shorten the monitoring cycle, adapt to total pressure fluctuations, and improve measurement accuracy while considering Y capacitors.

[0006] Therefore, a method for monitoring and identifying insulation resistance parameters of electric vehicles is needed. Summary of the invention

[0007] The present invention provides a method and system for monitoring and identifying insulation resistance parameters of an electric vehicle, so as to solve the problem of how to accurately monitor the insulation resistance parameters of an electric vehicle.

[0008] In order to solve the above problem, according to one aspect of the present invention, a method for monitoring and identifying insulation resistance parameters of an electric vehicle is provided, the method comprising:

[0009] Establishing an electric vehicle insulation resistance monitoring circuit model; wherein the electric vehicle insulation resistance monitoring circuit model comprises a square wave injection source for exciting a monitoring signal and a bridge arm branch for introducing a square wave signal, and a bridge arm resistor is connected in series to the bridge arm branch for capturing the monitoring signal;

[0010] By using Laplace transform, the electric vehicle insulation resistance monitoring circuit model is transformed into an operational circuit model in the complex frequency domain for calculation, and an algebraic equation for the correlation between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle is established;

[0011] Applying a linearly changing voltage of the power battery pack and a square wave voltage injected by a square wave source to the electric vehicle insulation resistance monitoring circuit model at the same time, and performing voltage sampling, identifying circuit parameters by a nonlinear least squares method based on the algebraic equation, and forming an objective function with the goal of minimizing the sum of squares of errors between the sampled value and the calculated value;

[0012] Based on the Gauss-Newton iteration method, an optimal solution of the circuit parameters that minimizes the objective function is obtained to obtain the monitoring value of the insulation resistance.

[0013] Preferably, the electric vehicle insulation resistance monitoring circuit model comprises: a DC power supply V s , positive electrode insulation resistance R1, negative electrode insulation resistance R2, positive electrode capacitance C1, negative electrode capacitance C2, positive electrode bridge arm resistance R3 and negative electrode bridge arm resistance R4, square wave injection source V g And the injection source matching small resistor R e ; Among them, the positive electrode insulation resistance R1 and the positive electrode capacitor C1 are connected to the DC power supply V s The positive electrode of the capacitor is connected to the ground, and the negative electrode insulation resistor R2 and the negative electrode capacitor C2 are connected to the DC power supply V s The negative pole of the bridge arm is connected to the ground, and one end of the positive bridge arm resistor R3 is connected to the DC power supply V s The other end of the positive bridge arm resistor R3 is connected to the square wave injection source V g One end of the negative bridge arm resistor R4 is connected to the DC power supply V sThe negative electrode of the bridge arm resistor R4 is connected to the negative electrode of the square wave injection source V g One end is connected to the square wave injection source V g The other end is injected through the source supporting small resistor R e Ground.

[0014] Preferably, the algebraic equation for the correlation between the voltage of the bridge arm resistor and the insulation resistance of the electric vehicle is: In the form of, u(t) is the bridge arm voltage, t is the time, x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively, and parameter identification is to identify x1, x2, x3 and x4, and then obtain the positive electrode insulation resistance and negative electrode insulation resistance.

[0015] Preferably, the square sum function of the sampled value and the calculated value error is:

[0016]

[0017] Where m is the number of sampling points; u i is the sampling value of the i-th sampling point; u'(t i ,X) is the calculated value of the i-th sampling point; x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively.

[0018] Preferably, when the Gauss-Newton iteration method is used for solving, the iteration formula is:

[0019]

[0020] Set the iteration termination error limit ε to satisfy ||ΔX||2=||[J T (X k )J(X k )] -1 JT(X k )E(X k )||2<ε, the iteration is terminated, and the optimal solution of parameter identification can be obtained: X=[x1, x2, x3, x4] T , the corresponding insulation resistances R1 and R2 can be obtained by reverse solution;

[0021] Where ΔX is the iterative step vector for solving the parameter vector X; X k is the calculation result of the parameter vector X in the kth iteration step; J(X k ) is about u(t i ,X k )’s Jacobian matrix; E(X k ) is the time domain sampling value u of the bridge arm voltage V(t) iCompared with the model calculated value u′(t i ,X k ) is the residual matrix composed of the difference between .

[0022] According to another aspect of the present invention, a system for monitoring and identifying insulation resistance parameters of an electric vehicle is provided, the system comprising:

[0023] A model building unit is used to build an electric vehicle insulation resistance monitoring circuit model; wherein the electric vehicle insulation resistance monitoring circuit model includes a square wave injection source for exciting a monitoring signal and a bridge arm branch for introducing a square wave signal, and a bridge arm resistor is connected in series to the bridge arm branch to capture the monitoring signal;

[0024] A model conversion unit, used to convert the electric vehicle insulation resistance monitoring circuit model into an operational circuit model in the complex frequency domain by Laplace transformation for calculation, and to establish an algebraic equation for the correlation between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle;

[0025] An objective function determination unit is used to simultaneously apply a voltage in the form of a linear change of a power battery pack and a square wave voltage injected by a square wave source to the electric vehicle insulation resistance monitoring circuit model, and perform voltage sampling, identify circuit parameters by a nonlinear least squares method based on the algebraic equation, and form an objective function with the goal of minimizing the sum of squares of errors between sampling values ​​and calculated values;

[0026] The monitoring value acquisition unit is used to obtain the optimal solution of the circuit parameters that minimizes the objective function based on the Gauss-Newton iteration method, so as to obtain the monitoring value of the insulation resistance.

[0027] Preferably, the electric vehicle insulation resistance monitoring circuit model comprises: a DC power supply V s , positive electrode insulation resistance R1, negative electrode insulation resistance R2, positive electrode capacitance C1, negative electrode capacitance C2, positive electrode bridge arm resistance R3 and negative electrode bridge arm resistance R4, square wave injection source V g And the injection source matching small resistor R e ; Among them, the positive electrode insulation resistance R1 and the positive electrode capacitor C1 are connected to the DC power supply V s The positive electrode of the capacitor is connected to the ground, and the negative electrode insulation resistor R2 and the negative electrode capacitor C2 are connected to the DC power supply V s The negative pole of the bridge arm is connected to the ground, and one end of the positive bridge arm resistor R3 is connected to the DC power supply V s The other end of the positive bridge arm resistor R3 is connected to the square wave injection source V g One end of the negative bridge arm resistor R4 is connected to the DC power supply V s The negative electrode of the bridge arm resistor R4 is connected to the negative electrode of the square wave injection source V g One end is connected to the square wave injection source Vg The other end is injected through the source supporting small resistor R e Ground.

[0028] Preferably, in the model conversion unit, the algebraic equation for the correlation between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle is: In the form of, u(t) is the bridge arm voltage, t is the time, x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively, and parameter identification is to identify x1, x2, x3 and x4, and then obtain the positive electrode insulation resistance and negative electrode insulation resistance.

[0029] Preferably, in the objective function determination unit, the square sum function of the sampled value and the calculated value error is:

[0030]

[0031] Where m is the number of sampling points; u i is the sampling value of the i-th sampling point; u'(t i ,X) is the calculated value of the i-th sampling point; x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively.

[0032] Preferably, when the monitoring value acquisition unit adopts the Gauss-Newton iteration method to solve, the iteration formula is:

[0033]

[0034] Set the iteration termination error limit ε to satisfy ||ΔX2=||[J T (X k )J(X k )] -1 J T (X k )E(X k )||2<ε, the iteration is terminated, and the optimal solution of parameter identification can be obtained: X=[x1, x2, x3, x4] T , the corresponding insulation resistances R1 and R2 can be obtained by reverse solution;

[0035] Where ΔX is the iterative step vector for solving the parameter vector X; X k is the calculation result of the parameter vector X in the kth iteration step; J(X k ) is about u(t i ,X k )’s Jacobian matrix; E(X k ) is the time domain sampling value u of the bridge arm voltage V(t) i Compared with the model calculated value u′(ti ,X k ) is the residual matrix composed of the difference between .

[0036] The present invention provides an electric vehicle insulation resistance parameter monitoring and identification method and system, comprising: establishing an electric vehicle insulation resistance monitoring circuit model; wherein the electric vehicle insulation resistance monitoring circuit model includes a square wave injection source for exciting a monitoring signal and a bridge arm branch for introducing a square wave signal, and a bridge arm resistor is connected in series on the bridge arm branch to capture the monitoring signal; using Laplace transform, the electric vehicle insulation resistance monitoring circuit model is converted into an operational circuit model in the complex frequency domain for calculation, and an algebraic equation of the correlation between the voltage of the bridge arm resistor and the insulation resistance of the electric vehicle is established; the electric vehicle insulation resistance monitoring circuit model is simultaneously applied with a linearly changing voltage of a power battery pack and a square wave voltage injected by a square wave source, and voltage sampling is performed; based on the algebraic equation, the circuit parameters are identified by a nonlinear least squares method, and an objective function is formed with the minimum square sum of the error between the sampling value and the calculated value as the goal; based on the Gauss-Newton iteration method, the optimal solution of the circuit parameters that minimizes the objective function is obtained to obtain the monitoring value of the insulation resistance. The present invention can obtain the monitoring value of the insulation resistance in a relatively short time when the electric vehicle has a large Y capacitor and the voltage of the power battery pack changes linearly, and has a high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0038] Figure 1 Flow chart of an electric vehicle insulation resistance parameter monitoring and identification method 100 according to an embodiment of the present invention;

[0039] Figure 2 A diagram of an insulation resistance monitoring model for an electric vehicle according to an embodiment of the present invention;

[0040] Figure 3 An equivalent schematic diagram of an insulation resistance monitoring circuit for an electric vehicle according to an embodiment of the present invention;

[0041] Figure 4 Schematic diagram of a computational circuit model for monitoring insulation resistance of an electric vehicle in a complex frequency domain according to an embodiment of the present invention;

[0042] Figure 5 is a flowchart of an iteration of nonlinear least squares parameter identification according to an embodiment of the present invention;

[0043] Figure 6 Schematic diagram of the structure of an electric vehicle insulation resistance parameter monitoring and identification system 600 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0045] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0046] Figure 1 FIG. 1 is a flow chart of an electric vehicle insulation resistance parameter monitoring and identification method 100 according to an embodiment of the present invention. Figure 1 As shown, the electric vehicle insulation resistance parameter monitoring and identification method provided by the embodiment of the present invention can obtain the monitoring value of the insulation resistance in a relatively short time and has high accuracy when the electric vehicle has a large Y capacitance and the voltage of the power battery pack changes linearly. The electric vehicle insulation resistance parameter monitoring and identification method 100 provided by the embodiment of the present invention starts from step 101. In step 101, an electric vehicle insulation resistance monitoring circuit model is established; wherein the electric vehicle insulation resistance monitoring circuit model includes a square wave injection source for exciting a monitoring signal and a bridge arm branch for introducing a square wave signal, and a bridge arm resistor is connected in series to the bridge arm branch to capture the monitoring signal.

[0047] Preferably, the electric vehicle insulation resistance monitoring circuit model comprises: a DC power supply V s , positive electrode insulation resistance R1, negative electrode insulation resistance R2, positive electrode capacitance C1, negative electrode capacitance C2, positive electrode bridge arm resistance R3 and negative electrode bridge arm resistance R4, square wave injection source V g And the injection source matching small resistor R e ; Among them, the positive electrode insulation resistance R1 and the positive electrode capacitor C1 are connected to the DC power supply V s The positive electrode of the capacitor is connected to the ground, and the negative electrode insulation resistor R2 and the negative electrode capacitor C2 are connected to the DC power supply V s The negative pole of the bridge arm is connected to the ground, and one end of the positive bridge arm resistor R3 is connected to the DC power supply V s The other end of the positive bridge arm resistor R3 is connected to the square wave injection source V g One end of the negative bridge arm resistor R4 is connected to the DC power supply V sThe negative electrode of the bridge arm resistor R4 is connected to the negative electrode of the square wave injection source V g One end is connected to the square wave injection source V g The other end is injected through the source supporting small resistor R e Ground.

[0048] In the present invention, an electric vehicle insulation resistance monitoring circuit model is established by taking into account the Y capacitor and the linear change of the power battery voltage. Figure 2 As shown, the power battery pack in the DC power system of the electric vehicle is the power source V s , R1 and R2 are respectively the positive insulation resistance and the negative insulation resistance, C1 and C2 are respectively the positive capacitance and the negative capacitance, R3 and R4 are respectively the positive bridge arm resistance and the negative bridge arm resistance, the bridge arm resistance is a known resistance in the device, V g is the square wave injection source, R e A small resistor is provided for the injection source. The equivalent model of the electric vehicle insulation resistance monitoring circuit is as follows: Figure 3 As shown, it includes a power battery, a positive insulation resistor, a negative insulation resistor, a positive capacitor, a negative capacitor, a positive bridge arm resistor, a negative bridge arm resistor, a bridge neutral point, and a square wave voltage injection source. The connection relationship between the components is: there is no actual component corresponding to the neutral point of the bridge, it is only set for the convenience of discussion, and it is connected to the positive bridge arm resistor, the negative bridge arm resistor, and the square wave voltage injection source respectively. In the circuit model, the positive insulation resistor is between the positive electrode of the power battery and the casing, the negative insulation resistor is between the negative electrode of the power battery and the casing, and the positive capacitor is between the positive electrode of the power battery and the casing; the negative capacitor is between the negative electrode of the power battery and the casing, the positive bridge arm resistor is between the positive electrode of the power battery and the neutral point of the bridge, and the negative bridge arm resistor is between the negative electrode of the power battery and the neutral point of the bridge.

[0049] In step 102, Laplace transform is used to transform the electric vehicle insulation resistance monitoring circuit model into an operational circuit model in the complex frequency domain for calculation, and an algebraic equation for the correlation between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle is established.

[0050] Preferably, the algebraic equation for the correlation between the voltage of the bridge arm resistor and the insulation resistance of the electric vehicle is: In the form of, u(t) is the bridge arm voltage, t is the time, x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively, and parameter identification is to identify x1, x2, x3 and x4, and then obtain the positive electrode insulation resistance and negative electrode insulation resistance.

[0051] In the present invention, Laplace transform is used to transform the electric vehicle insulation resistance monitoring circuit model into an operational circuit model in the complex frequency domain. The operational circuit model is as follows: Figure 4 As shown, Figure 4 The meaning and parameters of each component in Figure 2 Same, only Figure 2 The algebraic equation of the relationship between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle is established based on the operational circuit model in the complex frequency domain. Among them, the algebraic equation of the relationship between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle is the equation of the bridge arm voltage with respect to the time quantity, which is of the form In the form of, u(t) is the bridge arm voltage, t is the time, x1, x2, x3, x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance, and negative electrode capacitance. Parameter identification is to identify x1, x2, x3, x4, and then obtain the positive and negative electrode insulation resistance.

[0052] In step 103, a linearly varying voltage of the power battery pack and a square wave voltage injected by a square wave source are simultaneously applied to the electric vehicle insulation resistance monitoring circuit model, and voltage sampling is performed. Based on the algebraic equation, circuit parameters are identified by a nonlinear least squares method, and an objective function is formed with the goal of minimizing the sum of squares of errors between sampled values ​​and calculated values.

[0053] Preferably, the square sum function of the sampled value and the calculated value error is:

[0054]

[0055] Where m is the number of sampling points; u i is the sampling value of the i-th sampling point; u'(t i ,X) is the calculated value of the i-th sampling point; x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively.

[0056] In step 104, an optimal solution of the circuit parameters that minimizes the objective function is obtained based on the Gauss-Newton iteration method to obtain a monitoring value of the insulation resistance.

[0057] Preferably, when the Gauss-Newton iteration method is used for solving, the iteration formula is:

[0058]

[0059] Set the iteration termination error limit ε to satisfy ||ΔX||2=||[J T (X k )J(X k )] -1 J T (X k )E(X k)||2<ε, the iteration is terminated, and the optimal solution of parameter identification can be obtained: X=[x1, x2, x3, x4] T , the corresponding insulation resistances R1 and R2 can be obtained by reverse solution;

[0060] Where ΔX is the iterative step vector for solving the parameter vector X; X k is the calculation result of the parameter vector X in the kth iteration step; J(X k ) is about u(t i ,X k )’s Jacobian matrix; E(X k ) is the time domain sampling value u of the bridge arm voltage V(t) i Compared with the model calculated value u′(t i ,X k ) is the residual matrix composed of the difference between .

[0061] In the present invention, the power battery pack voltage and the injected square wave voltage are simultaneously applied to the electric vehicle insulation resistance monitoring circuit model, and the bridge arm voltage is continuously sampled at a certain interval, thereby obtaining the sampling points of the bridge arm voltage and time. Among them, a square wave is generated at a certain period, the duty cycle is 50%, a high level is generated in half a period, and a low level is generated in half a period, and the circuit model is between the neutral point and the casing. Based on the algebraic equation, the circuit parameters are identified by the nonlinear least squares method, and the objective function is formed with the goal of minimizing the sum of squares of the sampled value and the calculated value errors. Among them, the function of the sum of squares of the sampled value and the calculated value errors is: Where m is the number of sampling points; u i is the sampling value of the i-th sampling point; u'(t i , X) is the calculated value of the i-th sampling point; x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively. Finally, based on the Gauss-Newton iteration method, the optimal solution of the circuit parameters that minimizes the objective function is obtained to obtain the monitoring value of the insulation resistance.

[0062] Among them, when the Gauss-Newton iteration method is used for solving, the iteration formula is:

[0063]

[0064] Set the iteration termination error limit ε to satisfy ||ΔX||2=||[J T (X k )J(X k )] -1 J T (X k )E(X k )||2<ε, the iteration is terminated, and the optimal solution of parameter identification can be obtained: X=[x1, x2, x3, x4]T , the corresponding insulation resistances R1 and R2 can be obtained by inverse solution.

[0065] Compared with the existing electric vehicle insulation resistance monitoring, the present invention has the following advantages: when the electric vehicle has a large Y capacitance and a large insulation resistance value, the existing monitoring method takes a long time to measure and the measurement results are all too small, while the measurement time of the method described in the present invention is greatly shortened, and the measurement value will not be too low, thereby ensuring the measurement accuracy; during the braking or acceleration process of the electric vehicle, when the voltage changes linearly, the traditional method cannot measure at this time or the measurement result has a large deviation, while the method described in the present invention can still measure the insulation resistance value at this time and ensure a certain accuracy.

[0066] In the embodiment of the present invention, since the voltage of the power battery pack changes linearly, the changing voltage of the power battery pack is simulated as a straight line that rises at a certain slope with a certain initial voltage value. The initial voltage value is A2, the slope value is A1 / s, and the square wave injection source V g The first half cycle is V3, and the second half cycle is -V3. Under the superposition of the power battery pack voltage and the injected square wave voltage, the initial values ​​of capacitors C1 and C2 at time can be obtained according to the voltage superposition theorem. Since A3 is the absolute value of the voltage of the square wave signal injection source, at the moment of the injected square wave voltage change, it jumps from -A3V at time to A3V at time. Assume V s The initial voltage is 500V and increases by 5V per second, that is, A2 = 500V, A1 = 5V, V s =(A2+A1x)V; square wave injection source period T=10s, V3=20V, that is, square wave injection source V g The first 5s is 20V, and the last 5s is -20V; the bridge arm resistance is set to R3 = R4 = 5MΩ. e =20kΩ. The capacitance values ​​C1 and C2 to ground and the insulation resistance values ​​R1 and R2 are artificially set and measured by the method of the present invention.

[0067] In which, taking the square wave injection source switching from the second half cycle to the first half cycle as an example, the initial voltage value of capacitor C1 and the initial voltage value of capacitor C2 are respectively:

[0068]

[0069] Among them, λ1 is the offset degree of the bridge arm midpoint before the signal is injected, and λ2 is the voltage division degree of the insulation resistance before the power battery pack is pressurized, that is:

[0070]

[0071] exist Figure 4 In the equation, according to the node current law, taking node (0) as the reference point, the complex frequency domain equations for nodes (1), (2), and (3) are written as follows:

[0072]

[0073] To simplify the formula, let: Combine the above equations, and because Re is very small, usually in the kΩ level, and the positive and negative bridge arm resistors R3 and R4 are very large, usually in the MΩ level. So we can make an approximate treatment R e ≈0, then the voltage u3 on the resistor R3 can be obtained by rearranging:

[0074]

[0075] in,

[0076]

[0077] By changing the above formula back to the time domain formula, we can get the calculated value u' of the R3 voltage:

[0078]

[0079] The ADC chip collects the voltage on resistor R3 and obtains a series of voltage-time sampling points (t i ,u i ), i = 1, 2...m. According to the least squares method, the observed value is u i , the calculated value is u'(t i ,X), where X=[x 1, x 2, x 3, x4] T , then the residual matrix E is:

[0080]

[0081] According to the least squares method, the actual observation value u is required i and the calculated value u'(t i ,X) reaches the minimum. At this time, the parameter X is the optimal solution, and its error square sum function is L(u,u'(t,X)):

[0082]

[0083] In order to minimize the error sum of squares function, the necessary condition is:

[0084]

[0085] For this nonlinear least squares problem, the Gauss-Newton iteration method is used to solve it. The iteration formula is:

[0086]

[0087] in,

[0088]

[0089] Set the iteration termination error limit ε, the iteration flow chart is as follows Figure 5 As shown, the iteration is terminated when the following formula is satisfied:

[0090] ||ΔX||2=||[J T (X k )J(X k )] -1 J T (X k )E(X k )||2<ε,

[0091] The optimal solution of parameter identification can be obtained as X = [x1, x2, x3, x4] T , the corresponding insulation resistance R1 and R2 can be obtained by inverse solution. The relative error of the calculated results is shown in Table 1. The relative error value is the relative error between the actual value and the calculated value of the parallel value of R1 and R2.

[0092] Table 1 Insulation resistance calculation results

[0093] Relative error value / % C=0.1μF C=0.2μF C=0.4μF C=1μF C=2μF <![CDATA[R1=10MΩ,R2=1MΩ]]> 0.39 0.49 0.3 0.03 0.26 <![CDATA[R1=100MΩ,R2=1MΩ]]> 0.34 1.09 1.21 2.35 3.77 <![CDATA[R1=100MΩ,R2=10MΩ]]> 0.36 0.49 0.62 1.69 0.95

[0094] When the capacitance C=2 μF, the conventional insulation monitoring method (injection method) is compared with the method proposed in the present invention, as shown in Table 2.

[0095] Table 2 Relative error delta of the two methods when C = 2μF

[0096] Relative error value / % Traditional injection method Methods <![CDATA[R1=10MΩ,R2=1MΩ]]> 113.67 0.82 <![CDATA[R1=100MΩ,R2=1MΩ]]> 122.59 2.77 <![CDATA[R1=100MΩ,R2=10MΩ]]> 72.52 5.54

[0097] In order to obtain accurate results of insulation resistance in the shortest possible time when the voltage of the power battery pack changes linearly and the capacitor exists, the present invention no longer uses the traditional method of waiting for stability before measuring, but solves it through the dynamic equation of the circuit. First, a circuit model for monitoring the insulation resistance of electric vehicles is established. However, the dynamic equation of the circuit in the time domain is a differential equation, and the requirements for the ARM chip of the device are relatively high when solving it. Therefore, the present invention converts the time domain circuit model into an operational circuit model in the complex frequency domain, and converts the differential equation corresponding to the time domain circuit model into an algebraic equation in the complex frequency domain. Finally, a series of sampling points are obtained by collecting the bridge arm voltage, and the nonlinear least squares method is used for parameter identification to obtain the insulation resistance monitoring value as the circuit parameter. The results show that the electric vehicle insulation resistance monitoring method based on the complex frequency domain model and nonlinear least squares parameter identification adopted by the method of the present invention can obtain a more accurate insulation resistance monitoring value in a relatively short time.

[0098] Figure 6FIG. 6 is a schematic diagram of the structure of an electric vehicle insulation resistance parameter monitoring and identification system 600 according to an embodiment of the present invention. Figure 6 As shown, the electric vehicle insulation resistance parameter monitoring and identification system 600 provided in the embodiment of the present invention includes: a model building unit 601, a model conversion unit 602, an objective function determination unit 603 and a monitoring value acquisition unit 604.

[0099] Preferably, the model building unit 601 is used to build an electric vehicle insulation resistance monitoring circuit model; wherein the electric vehicle insulation resistance monitoring circuit model includes a square wave injection source for exciting a monitoring signal and a bridge arm branch for introducing a square wave signal, and a bridge arm resistor is connected in series in the bridge arm branch to capture the monitoring signal.

[0100] Preferably, the electric vehicle insulation resistance monitoring circuit model comprises: a DC power supply V s , positive electrode insulation resistance R1, negative electrode insulation resistance R2, positive electrode capacitance C1, negative electrode capacitance C2, positive electrode bridge arm resistance R3 and negative electrode bridge arm resistance R4, square wave injection source V g And the injection source matching small resistor R e ; Among them, the positive electrode insulation resistance R1 and the positive electrode capacitor C1 are connected to the DC power supply V s The positive electrode of the capacitor is connected to the ground, and the negative electrode insulation resistor R2 and the negative electrode capacitor C2 are connected to the DC power supply V s The negative pole of the bridge arm is connected to the ground, and one end of the positive bridge arm resistor R3 is connected to the DC power supply V s The other end of the positive bridge arm resistor R3 is connected to the square wave injection source V g One end of the negative bridge arm resistor R4 is connected to the DC power supply V s The negative electrode of the bridge arm resistor R4 is connected to the negative electrode of the square wave injection source V g One end is connected to the square wave injection source V g The other end is injected through the source supporting small resistor R e Ground.

[0101] Preferably, the model conversion unit 602 is used to use Laplace transform to convert the electric vehicle insulation resistance monitoring circuit model into an operational circuit model in the complex frequency domain for calculation, and establish an algebraic equation for the correlation between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle.

[0102] Preferably, in the model conversion unit 602, the algebraic equation of the correlation between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle is: In the form of, u(t) is the bridge arm voltage, t is the time, x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively, and parameter identification is to identify x1, x2, x3 and x4, and then obtain the positive electrode insulation resistance and negative electrode insulation resistance.

[0103] Preferably, the objective function determination unit 603 is used to simultaneously apply a linearly varying voltage of the power battery pack and a square wave voltage injected by a square wave source to the electric vehicle insulation resistance monitoring circuit model, and perform voltage sampling, identify circuit parameters through a nonlinear least squares method based on the algebraic equation, and form an objective function with the goal of minimizing the sum of squares of errors between the sampled values ​​and the calculated values.

[0104] Preferably, in the objective function determination unit 603, the square sum function of the sample value and the calculated value error is:

[0105]

[0106] Where m is the number of sampling points; u i is the sampling value of the i-th sampling point; u'(t i ,X) is the calculated value of the i-th sampling point; x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively.

[0107] Preferably, the monitoring value acquisition unit 604 is used to obtain an optimal solution of the circuit parameters that minimizes the objective function based on a Gauss-Newton iteration method, so as to obtain the monitoring value of the insulation resistance.

[0108] Preferably, when the monitoring value acquisition unit adopts the Gauss-Newton iteration method to solve, the iteration formula is:

[0109]

[0110] Set the iteration termination error limit ε to satisfy ||ΔX||2=||[J T (X k )J(X k )] -1 J T (X k )E(X k )||2<ε, the iteration is terminated, and the optimal solution of parameter identification can be obtained: X=[x1, x2, x3, x4] T , the corresponding insulation resistances R1 and R2 can be obtained by reverse solution;

[0111] Where ΔX is the iterative step vector for solving the parameter vector X; X kis the calculation result of the parameter vector X in the kth iteration step; J(X k ) is about u(t i ,X k )’s Jacobian matrix; E(X k ) is the time domain sampling value u of the bridge arm voltage V(t) i Compared with the model calculated value u′(t i ,X k ) is the residual matrix composed of the difference between .

[0112] The electric vehicle insulation resistance parameter monitoring and identification system 600 of the embodiment of the present invention corresponds to the electric vehicle insulation resistance parameter monitoring and identification method 100 of another embodiment of the present invention, and will not be described in detail herein.

[0113] The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

[0114] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise therein. All references to "a / said / the [means, components, etc.]" are to be openly interpreted as at least one instance of said means, components, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily have to be performed in the exact order disclosed, unless explicitly stated otherwise.

[0115] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring and identifying insulation resistance parameters of an electric vehicle, characterized in that: The method comprises: Establishing a circuit model of an electric vehicle insulation resistance monitoring circuit; wherein the electric vehicle insulation resistance monitoring circuit model comprises a square wave injection source for exciting a monitoring signal and a bridge arm branch for introducing a square wave signal, and a bridge arm resistor is connected in series to the bridge arm branch for capturing the monitoring signal; By using Laplace transform, the electric vehicle insulation resistance monitoring circuit model is transformed into an operational circuit model in the complex frequency domain for calculation, and an algebraic equation for the correlation between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle is established; Applying a linearly changing voltage of the power battery pack and a square wave voltage injected by a square wave source to the electric vehicle insulation resistance monitoring circuit model at the same time, and performing voltage sampling, identifying circuit parameters by a nonlinear least squares method based on the algebraic equation, and forming an objective function with the goal of minimizing the sum of squares of errors between the sampled value and the calculated value; Based on the Gauss-Newton iteration method, an optimal solution of the circuit parameters that minimizes the objective function is obtained to obtain the monitoring value of the insulation resistance.

2. The method according to claim 1, characterized in that The electric vehicle insulation resistance monitoring circuit model includes: a DC power supply V s , positive electrode insulation resistance R1, negative electrode insulation resistance R2, positive electrode capacitance C1, negative electrode capacitance C2, positive electrode bridge arm resistance R3 and negative electrode bridge arm resistance R4, square wave injection source V g And the injection source matching small resistor R e ; Among them, the positive electrode insulation resistance R1 and the positive electrode capacitor C1 are connected to the DC power supply V s The positive electrode of the capacitor is connected to the ground, and the negative electrode insulation resistor R2 and the negative electrode capacitor C2 are connected to the DC power supply V s The negative pole of the bridge arm is connected to the ground, and one end of the positive bridge arm resistor R3 is connected to the DC power supply V s The other end of the positive bridge arm resistor R3 is connected to the square wave injection source V g One end of the negative bridge arm resistor R4 is connected to the DC power supply V s The negative electrode of the bridge arm resistor R4 is connected to the negative electrode of the square wave injection source V g One end is connected to the square wave injection source V g The other end is injected through the source supporting small resistor R e Ground.

3. The method according to claim 2, characterized in that The algebraic equation for the relationship between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle is: In the form of, u(t) is the bridge arm voltage, t is the time, x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively, and parameter identification is to identify x1, x2, x3 and x4, and then obtain the positive electrode insulation resistance and negative electrode insulation resistance.

4. The method according to claim 2, characterized in that: The square sum function of the sampled value and the calculated value error is: Where m is the number of sampling points; u i is the sampling value of the i-th sampling point; u'(t i ,X) is the calculated value of the i-th sampling point; x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively.

5. The method according to claim 2, characterized in that: When the Gauss-Newton iteration method is used for solving, the iteration formula is: Set the iteration termination error limit ε to satisfy ||ΔX||2=||[J T (X k )J(X k )] -1 J T (X k )E(X k )||2<ε, the iteration is terminated, and the optimal solution of parameter identification can be obtained: X=[x1, x2, x3, x4] T , the corresponding insulation resistances R1 and R2 can be obtained by reverse solution; Where ΔX is the iterative step vector for solving the parameter vector X; X k is the calculation result of the parameter vector X in the kth iteration step; J(X k ) is about u(t i ,X k )’s Jacobian matrix; E(X k ) is the time domain sampling value u of the bridge arm voltage V(t) i Compared with the model calculated value u′(t i ,X k ) is the residual matrix composed of the difference between .

6. An electric vehicle insulation resistance parameter monitoring and identification system, characterized in that: The system comprises: A model building unit is used to build an electric vehicle insulation resistance monitoring circuit model; wherein the electric vehicle insulation resistance monitoring circuit model includes a square wave injection source for exciting a monitoring signal and a bridge arm branch for introducing a square wave signal, and a bridge arm resistor is connected in series to the bridge arm branch to capture the monitoring signal; A model conversion unit, used to convert the electric vehicle insulation resistance monitoring circuit model into an operational circuit model in the complex frequency domain by Laplace transformation for calculation, and to establish an algebraic equation for the correlation between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle; An objective function determination unit is used to simultaneously apply a voltage in the form of a linear change of a power battery pack and a square wave voltage injected by a square wave source to the electric vehicle insulation resistance monitoring circuit model, and perform voltage sampling, identify circuit parameters by a nonlinear least squares method based on the algebraic equation, and form an objective function with the goal of minimizing the sum of squares of errors between sampling values ​​and calculated values; The monitoring value acquisition unit is used to obtain the optimal solution of the circuit parameters that minimizes the objective function based on the Gauss-Newton iteration method, so as to obtain the monitoring value of the insulation resistance.

7. The system according to claim 6, characterized in that The electric vehicle insulation resistance monitoring circuit model includes: a DC power supply V s , positive electrode insulation resistance R1, negative electrode insulation resistance R2, positive electrode capacitance C1, negative electrode capacitance C2, positive electrode bridge arm resistance R3 and negative electrode bridge arm resistance R4, square wave injection source V g And the injection source matching small resistor R e ; Among them, the positive electrode insulation resistance R1 and the positive electrode capacitor C1 are connected to the DC power supply V s The positive electrode of the capacitor is connected to the ground, and the negative electrode insulation resistor R2 and the negative electrode capacitor C2 are connected to the DC power supply V s The negative pole of the bridge arm is connected to the ground, and one end of the positive bridge arm resistor R3 is connected to the DC power supply V s The other end of the positive bridge arm resistor R3 is connected to the square wave injection source V g One end of the negative bridge arm resistor R4 is connected to the DC power supply V s The negative electrode of the bridge arm resistor R4 is connected to the negative electrode of the square wave injection source V g One end is connected to the square wave injection source V g The other end is injected through the source supporting small resistor R e Ground.

8. The system according to claim 7, characterized in that In the model conversion unit, the algebraic equation for the correlation between the voltage of the bridge arm resistance and the insulation resistance of the electric vehicle is: In the form of, u(t) is the bridge arm voltage, t is the time, x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively, and parameter identification is to identify x1, x2, x3 and x4, and then obtain the positive electrode insulation resistance and negative electrode insulation resistance.

9. The system according to claim 7, characterized in that In the objective function determination unit, the square sum function of the sampled value and the calculated value error is: Where m is the number of sampling points; u i is the sampling value of the i-th sampling point; u'(t i ,X) is the calculated value of the i-th sampling point; x1, x2, x3 and x4 are the algebraic expressions formed by the positive electrode insulation resistance, negative electrode insulation resistance, positive electrode capacitance and negative electrode capacitance respectively.

10. The system according to claim 7, characterized in that When the monitoring value acquisition unit adopts the Gauss-Newton iteration method to solve, the iteration formula is: Set the iteration termination error limit ε to satisfy ||ΔX||2=||[J T (X k )J(X k )] -1 J T (X k )E(X k )||2<ε, the iteration is terminated, and the optimal solution of parameter identification can be obtained: X=[x1, x2, x3, x4] T , the corresponding insulation resistances R1 and R2 can be obtained by reverse solution; Where ΔX is the iterative step vector for solving the parameter vector X; X k is the calculation result of the parameter vector X in the kth iteration step; J(X k ) is about u(t i ,X k )’s Jacobian matrix; E(X k ) is the time domain sampling value u of the bridge arm voltage V(t) i Compared with the model calculated value u′(t i ,X k ) is the residual matrix composed of the difference between .

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