CN114355048B - Insulation detection method and system for electric automobile - Google Patents
Insulation detection method and system for electric automobile Download PDFInfo
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- CN114355048B CN114355048B CN202111633207.9A CN202111633207A CN114355048B CN 114355048 B CN114355048 B CN 114355048B CN 202111633207 A CN202111633207 A CN 202111633207A CN 114355048 B CN114355048 B CN 114355048B
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Abstract
The application provides an electric automobile power battery's insulation detection method, the electric bridge circuit based on connecting between power battery's positive and negative pole and chassis ground predicts power battery's positive and negative pole to chassis ground's equivalent insulation resistance's resistance and equivalent Y capacitance's capacitance value, includes: respectively acquiring sampling voltage values on a first resistor and a second resistor in the states that a first switch is opened or closed and a second switch is opened or closed; the sampling voltage values comprise a first voltage value, a second voltage value, a third voltage value and a fourth voltage value; obtaining first, second, third and fourth predicted stable values of the positive and negative sampling voltages according to the first, second, third and fourth voltage values and the trained first genetic optimization BP neural network; and determining the resistance values of the positive and negative equivalent insulation resistors according to the first, second, third and fourth predicted stable values of the positive and negative sampling voltages. And determining the capacitance values of the positive and negative electrode equivalent Y capacitors according to the resistance values of the positive and negative electrode equivalent insulation resistors.
Description
Technical Field
The application relates to the field of electric automobile detection, in particular to an insulation detection method and system of an electric automobile.
Background
The voltage range of the power battery of the electric automobile is generally more than 300V, the insulation performance between the power battery and the chassis of the automobile body is reduced due to the complex working environment, and safety accidents such as electric leakage, fire and the like can occur in serious cases, so that the safety of drivers and passengers is threatened. The method has the advantages that the insulation resistance of the power battery of the electric automobile is accurately detected in real time, and the method has important significance for improving the safety of the electric automobile.
Due to factors such as artificial design and self structure, Y capacitance exists between a high-voltage bus and a chassis ground of the electric automobile. In the insulation resistance detection, the charging and discharging process of the Y capacitor not only reduces the response speed of a detection system, but also influences sampling signals, and therefore errors occur in the calculation of the insulation resistance value of the ground.
In the insulation detection circuit test model shown in fig. 1, the power battery U is set to 300V, and the switches K3 and K4 control whether the equivalent Y capacitors Cp and Cn are connected to the circuit.
K3 and K4 were disconnected so that the Y capacitor was not connected to the circuit, and the test results are shown in FIG. 2. As can be seen from the figure, since the interference of the Y capacitor is avoided, the sampling voltage is a square wave signal, and the accuracy of the calculation result can be ensured by calculating the insulation resistance value by using the voltage value at any time.
Closing K3 and K4 puts the Y capacitor into circuit and the test results are shown in FIG. 3. It can be seen from the figure that due to the presence of the Y capacitor, the sampled voltage is no longer a square wave signal, but has a significant variation that takes some time to reach a plateau.
According to the test results, when the sampling voltage does not reach a stable value, the insulation resistance value is calculated, so that the insulation detection system is inaccurate, and a false alarm phenomenon occurs. If the calculation is carried out when the sampling voltage is stable, the measurement time is increased, and the response speed of the detection system is reduced. Therefore, how to effectively reduce the influence of the Y capacitor and quickly identify the stability of the sampling voltage when the voltage fluctuates has important significance on the detection of the insulation resistance.
Disclosure of Invention
In order to achieve the purpose, the application provides an insulation detection method and system of an electric automobile.
In a first aspect, the application provides an insulation detection method for an electric vehicle, which predicts equivalent insulation resistance R of a positive electrode and a negative electrode of a power battery to a chassis ground based on a bridge circuit connected between the positive electrode and the negative electrode of the power battery and the chassis ground p And R n Resistance value and equivalent Y capacitance C p And C n The bridge circuit comprises at least: a first resistor R 1 And a first switch K 1 A second resistor R connected between the positive pole of the power battery and the chassis ground in parallel 2 And a second switch K 2 The method is connected between the negative pole of the power battery and the chassis ground in parallel, and comprises the following steps: at the first switch K 1 Second switch K for opening or closing 2 Respectively obtaining the first resistance R in the open or closed state 1 A second resistor R 2 A sampled voltage value of; the sampling voltage values comprise a first voltage value, a second voltage value, a third voltage value and a fourth voltage value; obtaining positive and negative electrode samples according to the first, second, third and fourth voltage values and the trained first genetic optimization BP neural networkFirst, second, third, and fourth predicted stable values of the sample voltage; determining the equivalent positive and negative insulation resistance R of the positive and negative poles of the power battery to the chassis ground according to the first, second, third and fourth predicted stable values of the positive and negative sampling voltages p And R n The resistance value of (1); according to the equivalent insulation resistance R of the positive electrode and the negative electrode p And R n The positive electrode equivalent Y capacitor C and the negative electrode equivalent Y capacitor C of the power battery are determined through a trained second genetic optimization BP neural network p And C n The capacitance value of (2).
As a preferred embodiment, said first switch K 1 Second switch K for opening or closing 2 Respectively obtaining the first resistance R in the open or closed state 1 A second resistor R 2 A sampled voltage value comprising: at the first switch K 1 Closed and the second switch K 2 In the off state, the first resistance R is obtained 1 A second resistor R 2 The corresponding first and second voltage values; at the first switch K 1 Off and the second switch K 2 In the closed state, the first resistance R is obtained 1 A second resistor R 2 The third and fourth voltage values are corresponding; wherein the first, second, third and fourth voltage values are in the equivalent Y capacitor C p And C n Under the influence of the first resistance R 1 A second resistor R 2 The sampled voltage of (c).
As a preferred embodiment, the method further comprises: according to the equivalent insulation resistance R of the positive electrode and the negative electrode p And R n The resistance value of the anode and the cathode is determined.
As a preferred embodiment, the method further comprises: respectively obtaining the first resistors R 1 A second resistor R 2 And comparing the difference value of the first voltage value and the second voltage value with a set value according to the corresponding first voltage value and second voltage value, and determining that the insulation fault exists under the condition that the difference value is greater than the set value according to the comparison result.
According to the equivalent insulation resistance R p And R n The positive electrode and the negative electrode equivalent Y capacitor C are determined through a trained second genetic optimization BP neural network p And C n Comprises: according to the equivalent insulation resistance R of the positive pole and the negative pole of the power battery to the chassis ground p And R n Comparing with a set value, and determining an insulation fault; under the condition of insulation fault, acquiring the equivalent insulation resistance R of the positive electrode and the negative electrode with the insulation fault for n times in unit time t p And R n An arithmetic mean of the resistance values; the value of n is a natural number; based on the equivalent insulation resistance R of the positive electrode and the negative electrode p And R n The arithmetic mean value of the resistance value and the sampling voltage value are used for determining the equivalent Y capacitor C of the positive electrode and the negative electrode through a trained second genetic optimization BP neural network p And C n The capacitance value of (2).
As a preferred embodiment, the method further comprises: equivalent insulation resistance R between the positive pole and the negative pole of the power battery and the chassis ground p And R n Is less than a set value, it is determined that there is an insulation fault.
As a preferred embodiment, the method further comprises: according to the equivalent insulation resistance R of the positive electrode and the negative electrode p And R n Calculating the insulation strength values of the positive electrode and the negative electrode; equivalent insulation resistance R between the positive pole and the negative pole of the power battery and the chassis ground p And R n Determining that an insulation fault exists when one of the resistance values of the capacitor is smaller than a set value; under the condition that the insulation fault exists, acquiring the arithmetic mean value of the insulation strength of the positive electrode and the negative electrode of the insulation fault occurring n times in unit time t; and determining the grade of the insulation fault according to the arithmetic mean value of the insulation strength of the positive electrode and the insulation strength of the negative electrode.
As a preferred embodiment, said equivalent insulation resistance R is according to said p And R n The positive electrode and the negative electrode equivalent Y capacitor C are determined through a trained second genetic optimization BP neural network p And C n Comprises: according to the equivalent insulation resistance R of the positive pole and the negative pole of the power battery to the chassis ground p And R n Comparing with a set value, and determining that an insulation fault exists; in the presence of insulationUnder the condition of fault, acquiring the equivalent insulation resistance R of the positive electrode and the negative electrode with insulation fault for n times in unit time t p And R n An arithmetic mean of the resistance values; the value of n is a natural number; based on the equivalent insulation resistance R of the positive electrode and the negative electrode p And R n The arithmetic mean value of the resistance value and the sampling voltage value are used for determining the equivalent Y capacitor C of the positive electrode and the negative electrode through a trained second genetic optimization BP neural network p And C n The capacitance value of (2).
In a second aspect, the application provides an insulation detection system for an electric vehicle, which predicts equivalent insulation resistance R of a positive pole and a negative pole of a power battery to a chassis ground based on a bridge circuit connected between the positive pole and the negative pole of the power battery and the chassis ground p And R n Resistance value and equivalent Y capacitance C p And C n The bridge circuit comprises at least: a first resistor R 1 And a first switch K 1 A second resistor R connected between the positive pole of the power battery and the chassis ground in parallel 2 And a second switch K 2 Parallel to the negative pole of the power battery and the chassis ground, the system at least comprises: a signal acquisition module for the first switch K 1 Second switch K for opening or closing 2 Respectively obtaining the first resistance R in the open or closed state 1 A second resistor R 2 A sampled voltage value of; the sampling voltage values comprise a first voltage value, a second voltage value, a third voltage value and a fourth voltage value; the data processing module is used for obtaining first, second, third and fourth predicted stable values of the positive and negative sampling voltages according to the first, second, third and fourth voltage values and the trained first genetic optimization BP neural network; determining the equivalent insulation resistance R of the positive pole and the negative pole of the power battery to the positive pole and the negative pole of the chassis ground according to the first, second, third and fourth predicted stable values of the positive pole and the negative pole sampling voltages p And R n The resistance value of (1); according to the equivalent insulation resistance R of the positive electrode and the negative electrode p And R n The positive electrode equivalent Y capacitor C and the negative electrode equivalent Y capacitor C of the power battery are determined through a trained second genetic optimization BP neural network p And C n The capacitance value of (a); control moduleFor controlling the first switch K 1 Second switch K for opening or closing 2 Open or closed.
As a preferred embodiment, the signal acquisition module further comprises: a first sampling unit for sampling the first switch K 1 Closed and second switch K 2 In the off state, the first resistance R is obtained 1 A second resistor R 2 The corresponding first and second voltage values; a second sampling unit for sampling the first switch K 1 Off and the second switch K 2 In the closed state, the first resistance R is obtained 1 A second resistor R 2 The third and fourth voltage values are corresponding; wherein the first, second, third and fourth voltage values are in the equivalent Y capacitor C p And C n Under the influence of the first resistance R 1 A second resistor R 2 The sampled voltage of (c).
As a preferred embodiment, the data processing module further includes: an insulation strength determining unit for determining the equivalent insulation resistance R of the positive and negative electrodes p And R n Determining the insulation strength values of the positive electrode and the negative electrode by the resistance value; a fault determination unit for acquiring the first resistances R respectively 1 A second resistor R 2 Comparing the difference value of the first voltage value and the second voltage value with a set value according to the corresponding first voltage value and second voltage value, and determining that an insulation fault exists under the condition that the difference value is greater than the set value according to the comparison result; or equivalent insulation resistance R between the positive pole and the negative pole of the power battery and the chassis ground p And R n Determining that there is an insulation fault when one of the resistance values of (1) is less than a set value; the fault grade determining unit is used for acquiring the arithmetic mean value of the positive and negative insulation strengths of the insulation faults occurring n times in unit time t under the condition that the insulation faults exist; determining the grade of the insulation fault according to the arithmetic mean value of the insulation strength of the positive electrode and the negative electrode; an equivalent capacitance value prediction unit for obtaining the equivalent insulation resistance R of the positive and negative electrodes with insulation failure for n times in unit time t under the condition that the insulation failure exists p And R n An arithmetic mean of the resistance values; the value of n is a natural number; base ofAt the equivalent insulation resistance R of the positive and negative electrodes p And R n The arithmetic mean value of the resistance value and the sampling voltage value are used for determining the equivalent Y capacitor C of the positive electrode and the negative electrode through a trained second genetic optimization BP neural network p And C n The capacitance value of (2).
Drawings
In order to more clearly illustrate the technical solutions of the embodiments disclosed in the present specification, the drawings needed to be used in the description of the embodiments will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments disclosed in the present specification, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a test model of an insulation detection circuit provided in the background art;
fig. 2 is a schematic diagram of a test result of the insulation detection circuit provided in the background art when the Y capacitor is not connected to the circuit;
fig. 3 is a schematic diagram of a test result of the insulation detection circuit provided in the background art in the case where the Y capacitor is connected to the circuit;
fig. 4 is a schematic diagram of an insulation detection circuit of an electric vehicle according to an embodiment of the present application;
fig. 5 is a flowchart of an insulation detection method for an electric vehicle according to an embodiment of the present application;
fig. 6 is a frame diagram of an insulation detection system of an electric vehicle according to an embodiment of the present application;
fig. 7 is an insulation detection flowchart of the electric vehicle according to embodiment 1 of the present application.
Detailed Description
The technical solution of the present application is further described in detail by the accompanying drawings and embodiments.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
In the following description, references to the terms "first \ second \ third, etc. or module a, module B, module C, etc. are used solely to distinguish between similar objects and do not denote a particular order or importance to the objects, but rather the specific order or sequence may be interchanged as appropriate to enable embodiments of the application described herein to be practiced in an order other than that shown or described herein.
In the following description, reference to reference numerals indicating steps, such as S110, S120 … …, etc., does not necessarily indicate that the steps are performed in this order, and the order of the preceding and following steps may be interchanged or performed simultaneously, where permissible.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
Fig. 4 is a schematic diagram of an insulation detection circuit of an electric vehicle according to an embodiment of the present application. In FIG. 4, U is the power cell voltage; r p And R n Equivalent insulation resistances of the positive electrode and the negative electrode to the chassis ground respectively; resistance R 0 、R 1 、R 2 And switch K 1 、K 2 Form a bridge circuit, R 1 、R 2 Is a sampling resistor. Resistance R 0 、R 1 、R 2 Is formed by connecting a series of resistors with known resistance values in series. Will switch K 1 、K 2 Marked as a first switch and a second switch.
At time X, switch K is closed as shown in FIG. 4 1 Opening switch K 2 Sampling resistor R 1 、R 2 The predicted stable values of the sampling voltages at the two ends are respectively U 11 、U 21 (ii) a At the X +1 th moment, the switch K is turned off 1 Closing switch K 2 Collecting resistance R 1 、R 2 The predicted stable values of the sampling voltages at the two ends are respectively U 12 、U 22 . Can close the switch K 1 Opening switch K 2 Recording as a first state, turn off switch K 1 Closing switch K 2 And is recorded as the second state.
According to the basic principle of the circuit, the following steps are obtained:
the equivalent insulation resistance R of the positive and negative electrodes to the chassis ground can be solved by the simultaneous formula (1) — (4) p And R n Comprises the following steps:
therefore, the corresponding positive and negative electrode dielectric strength K p And K n Comprises the following steps:
to sum up, U is 11 、U 21 、U 12 、U 22 Substituting the values into equations (5) - (8) to calculate the positive and negative electrodesInsulation resistance R p And R n Value of (A) and corresponding dielectric strength K p And K n According to the positive and negative insulation resistance R p And R n A value of (b), and corresponding positive and negative electrode dielectric strengths K p And K n The value of (d) determines an insulation fault.
In one possible embodiment, the first state down-sampling resistor R may be implemented 1 、R 2 The sampling voltages at two ends are input into a trained genetic optimization BP neural network A to obtain a first prediction stable value U and a second prediction stable value U of the sampling voltages 11 、U 21 (ii) a Down-sampling resistor R in the second state 1 、R 2 The sampling voltages at two ends are input into a trained genetic optimization BP neural network A to obtain a third predicted stable value U and a fourth predicted stable value U of the predicted sampling voltages 12 、U 22 。
Based on the principle of the insulation detection circuit of the electric vehicle, the embodiment of the application provides an insulation detection method of the electric vehicle, which predicts the equivalent insulation resistance R of the positive pole and the negative pole of the power battery to the chassis ground based on the bridge circuit connected between the positive pole and the negative pole of the power battery and the chassis ground p And R n Resistance value and equivalent Y capacitance C p And C n Wherein the bridge circuit comprises at least: a first resistor R 1 And a first switch K 1 Connected in parallel between the positive pole of the power battery and the chassis ground, and a second resistor R 2 And a second switch K 2 The power battery is connected between the negative pole of the power battery and the chassis ground in parallel; at the first switch K 1 Second switch K for opening or closing 2 Respectively obtaining the first resistance R in the open or closed state 1 A second resistor R 2 A sampled voltage value of; the sampling voltage values comprise a first voltage value, a second voltage value, a third voltage value and a fourth voltage value; obtaining first, second, third and fourth prediction stable values U of the positive and negative sampling voltages according to the first, second, third and fourth voltage values and the trained first genetic optimization BP neural network 11 、U 21 、U 12 、U 22 (ii) a Determining the positive and negative electrodes of the power battery to the bottom according to the first, second, third and fourth predicted stable values of the positive and negative electrode sampling voltagesEquivalent insulation resistance R of positive electrode and negative electrode of disk ground p And R n The resistance value of (1); according to the equivalent insulation resistance R of the positive and negative electrodes p And R n The positive electrode and the negative electrode equivalent Y capacitor C of the power battery are determined through a trained second genetic optimization BP neural network p And C n The capacitance value of (2).
Fig. 5 is a flowchart of an insulation detection method for an electric vehicle according to an embodiment of the present application. The following specifically describes the insulation detection method of the electric vehicle provided in the embodiment of the present application with reference to fig. 4 and 5.
As shown in fig. 5, the insulation detection method for the electric vehicle provided by the embodiment of the present application can be implemented by the following steps S101 to S107.
S101, collecting sampling resistors R of the anode and the cathode of the power battery 1 And R 2 First and second voltage values.
In one possible embodiment, the sampling resistor R is obtained when the bridge switch is in the first state 1 And R 2 The corresponding first and second voltage values. Wherein the bridge switch comprises a first switch K 1 And a second switch K 2 The first state is that the first switch is closed and the second switch is open.
S102, judging whether the vehicle is possible to have insulation fault according to the first voltage value and the second voltage value, and executing S103-S107 under the condition that the vehicle is possible to have insulation fault; otherwise, returning to execute S101-S102
In one possible implementation, the resistance R is sampled while 1 And R 2 When the voltage difference value of the corresponding first and second voltage values is smaller than the set threshold, the vehicle has no insulation fault, and the sampling resistor R continues to be acquired 1 And R 2 Comparing the voltage values; when sampling resistor R 1 And R 2 When the difference between the first voltage and the second voltage is larger than the set value, the vehicle may have insulation fault.
S103, controlling and changing the opening and closing state of a bridge switch in the detection circuit; collecting and sampling resistor R 1 、R 2 And the third and fourth voltage values are corresponding.
In one implementation mode, the on-off state of the bridge switch is controlled to be changed, so that the bridge switch is changed from the first state to the second state, and the sampling resistor R is acquired when the bridge switch is in the second state 1 、R 2 And the third and fourth voltage values are corresponding. Wherein the second state is the first switch open and the second switch closed.
S104, according to the sampling resistance R 1 、R 2 And obtaining the predicted stable value of the sampling voltage by the corresponding first, second, third and fourth sampling voltage values and the trained genetic optimization BP neural network A. The genetically optimized BP neural network a may be denoted as a first genetically optimized BP neural network.
In an implementation mode, the first and second voltage values can be input into a trained genetic optimization BP neural network A, and a first and second predicted stable values U of the sampling voltage can be obtained 11 、U 21 。
Illustratively, the resistor R is sampled 1 、R 2 Inputting the corresponding first and second voltage values into the trained genetic optimization BP neural network A to obtain corresponding output values, and performing inverse normalization processing on the output values to obtain first and second predicted stable values U of the sampled voltage 11 、U 21 。
In an implementation mode, the third and fourth predicted stable values U of the sampled voltage can be obtained according to the third and fourth voltage values and the trained genetic optimization BP neural network a 12 、U 22 。
Illustratively, the resistance R will be sampled 1 、R 2 Inputting the corresponding third and fourth voltage values into the trained genetic optimization BP neural network A to obtain corresponding output values, and performing inverse normalization processing on the output values to obtain third and fourth predicted stable values U of the sampled voltage 12 、U 22 。
S105, predicting a stable value U according to the first, second, third and fourth sampling voltages output by the neural network 11 、U 21 、U 12 、U 22 For calculating power cellsEquivalent insulation resistance R of positive and negative electrodes to chassis ground p And R n According to R p And R n To determine the corresponding dielectric strength K p And K n The value of (c).
The calculation principle is shown in fig. 4, and the calculation process is shown in the above formula (1) -formula (8), which is not described herein again.
In summary, the first, second, third and fourth predicted stable values U of the sampled voltages are used 11 、U 21 、U 12 、U 22 Substituting the predicted values into equations (5) - (8) to calculate the insulation resistance R of the positive and negative electrodes p And R n According to R p And R n Can determine the corresponding dielectric strength K p And K n The value of (c).
S106, according to the equivalent insulation resistance R of the positive electrode and the negative electrode p And R n Resistance value or corresponding positive and negative electrode insulation strength K p And K n The value of (d) determines an insulation fault.
In one implementation, R may be p And R n And respectively comparing the values with the set insulation resistance values to judge whether the positive electrode and the negative electrode of the power battery of the electric automobile have insulation faults. If R is p And R n If the resistance values are all larger than the set value, the vehicle has no insulation fault, and the voltage values are continuously collected for comparison; if R is p And R n One of the insulation resistance values is smaller than the set insulation resistance value, which indicates that the vehicle has insulation fault.
In one implementation, K may be p And K n And comparing the values with the set insulation strength values respectively, and judging whether the positive electrode and the negative electrode of the power battery of the electric automobile have insulation faults or not. If K p And K n If the values of the voltage values are all larger than the set insulation strength value, the vehicle has no insulation fault, and the voltage values are continuously collected for comparison; if K p And K n One of the values of (a) is less than the set insulation strength value, indicating that an insulation fault has occurred in the vehicle.
In one possible embodiment, the insulation fault can occur n times per unit time tPositive and negative electrode insulation strength K p And K n Respectively taking an arithmetic mean value, comparing the arithmetic mean value with a set insulation strength value, and determining the insulation fault grade.
Illustratively, the threshold value of the primary insulation fault is set to be 1100 omega/V, and the threshold value of the secondary insulation fault is set to be 600 omega/V, namely when the insulation strength is greater than 1100 omega/V, the vehicle has no insulation fault; when the voltage is more than 600 omega/V and less than 1100 omega/V, the voltage is a primary insulation fault; when the voltage is less than 600 omega/V, the voltage is a secondary insulation fault.
In one implementation, different fault pre-warning operations may be performed according to the insulation fault level.
Illustratively, when the vehicle has no insulation fault, the insulation fault indicator lamp is not on, and the speech alarm is in a closed state; when a primary insulation fault occurs, the indicator lamp turns yellow and flickers at a lower frequency, and the voice alarm broadcasts the current fault level and warns people in the vehicle to pay attention to driving safety; when the second grade trouble appeared, then the pilot lamp turns red, and according to very fast frequency scintillation, voice alarm reports current fault level to personnel in the warning car in time overhauls.
In an implementation mode, when a fault occurs, the fault early warning module can be powered on; and (5) continuously generating faults for n times, and performing fault early warning according to the fault grade signal. Therefore, the response speed of the system can be improved, and the probability of false alarm can be reduced.
S107, according to the equivalent insulation resistance R of the positive electrode and the negative electrode p And R n The positive electrode and the negative electrode equivalent Y capacitor C of the power battery are determined through a trained second genetic optimization BP neural network p And C n The capacitance value of (2). And marking the genetic optimization BP neural network B as a second genetic optimization BP neural network.
In an implementation mode, n positive and negative insulation resistances R with insulation faults occurring n times in unit time t can be obtained p And R n A value of (d); the value of n is a natural number; respectively calculating the insulation resistance R of the positive electrode and the negative electrode under n faults p And R n The arithmetic mean of (a);inputting the arithmetic mean value of the positive and negative insulation resistance and the sampling voltage data into a trained genetic optimization BP neural network B, and outputting the capacitance value C of the positive and negative equivalent Y capacitor p And C n The capacitance value of (2).
Fig. 6 is a block diagram of an insulation detection system of an electric vehicle according to an embodiment of the present application. As shown in fig. 6, in order to avoid mutual interference, the insulation detection system is divided into the following modules according to different functions: the system comprises a power supply module 41, a control module 42, a signal acquisition module 43, a data processing module 44 and a fault early warning module 45. An isolation circuit is arranged between modules in the insulation detection system, so that mutual interference of different signals is reduced, and reliability and accuracy of information transmission between the modules are ensured. The hardware equipment of the insulation detection system of the electric automobile provided by the embodiment of the application adopts the material with better insulation and voltage resistance, and the insulation failure of the hardware equipment is prevented.
Each module is described in detail below in conjunction with fig. 4, 5, and 6.
And the power supply module 41 receives a state signal of a vehicle starting switch, and provides required electric energy for normal work of the control module 42, the signal acquisition module 43, the data processing module 44 and the fault early warning module 45 according to different working conditions.
In one possible implementation, the power supply module 41 is provided with a voltage conversion circuit to meet the power supply requirements of the different modules.
The control module 42 receives the fault determination signal a from the data processing module 44, and sends an instruction P1 to the signal acquisition module 43 according to different working conditions to control the first switch K 1 Second switch K for opening or closing 2 The open or closed state, and the control switching state of the bridge switch; and receiving the fault judgment signal B transmitted by the data processing module 44, sending a power-on instruction P2 to the power module 41, and controlling the power module 41 to supply power to the fault early warning module 45 according to the power-on instruction P2. Instruction P1 may be denoted as the first instruction and instruction P2 may be denoted as the second instruction.
In one possible embodiment, as shown in FIG. 4, the bridge switch includes a first switch K 1 And a second switch K 2 The control module 42 sends a command P10 to control the first switch K 1 Closed and second switch K 2 Off, this time in the first state; the control module 42 sends out an instruction P01 to control the first switch K after receiving the failure determination signal a sent from the data processing module 44 1 Opening and second switch K 2 Closed, this time in the second state.
In one possible implementation, the control module 42 may also control indicator lights through which the proper operation of the system is monitored in real time. Illustratively, when the power module 41 is off, the control module 42 issues a command P3 to control the indicator light not to be on; after the power module 41 is turned on, when each module of the insulation detection system works normally, the control module 42 sends out an instruction P4 to control the indicator light to be on normally; when one or more of the modules in the system fails to operate, the control module 42 issues a command P5 to control the indicator light to flash.
In one implementation, the signal acquisition module further includes: a first sampling unit for sampling at a first switch K 1 Closed and second switch K 2 In the off state, a first resistance R is obtained 1 A second resistor R 2 The corresponding first and second voltage values; a second sampling unit for sampling at the first switch K 1 Off and the second switch K 2 In the closed state, a first resistance R is obtained 1 A second resistor R 2 The third and fourth voltage values are corresponding; wherein the first, second, third and fourth voltage values are in the equivalent Y capacitor C p And C n First resistance R under influence 1 A second resistor R 2 The sampled voltage of (c). In one possible embodiment, the signal acquisition module 43 is provided with filteringThe circuit is used for reducing noise and electromagnetic interference in the circuit and improving sampling precision.
The data processing module 44 is used for obtaining first, second, third and fourth predicted stable values of the positive and negative sampling voltages according to the first, second, third and fourth voltage values and the trained first genetic optimization BP neural network; determining the equivalent insulation resistance R of the anode and the cathode of the power battery to the chassis ground according to the first, the second, the third and the fourth predicted stable values of the anode and the cathode sampling voltages p And R n The resistance value of (1); according to the equivalent insulation resistance R of the positive and negative electrodes p And R n The resistance value and the sampling voltage value of the power battery are determined through a trained second genetic optimization BP neural network p And C n The capacitance value of (2).
In this embodiment, the data processing module 44 is configured to receive the first resistance R 1 A second resistor R 2 And calculating a difference value of the corresponding first voltage value and the second voltage value, comparing the difference value of the first voltage value and the second voltage value with a set voltage difference value, and judging whether insulation faults possibly occur. Exemplarily, if the first resistance R 1 A second resistor R 2 If the difference value of the corresponding first and second voltage values is less than the set voltage difference value, the vehicle has no insulation fault, if the first resistor R 1 A second resistor R 2 If the difference between the corresponding first and second voltage values is greater than the set voltage difference, the vehicle may have an insulation fault, and a fault determination signal a is sent to the control module 42 under the condition that the vehicle may have an insulation fault. .
Under the condition that the vehicle is likely to have insulation fault, the data processing module 44 inputs the first and second voltage values into the trained genetic optimization BP neural network A to obtain first and second predicted stable values U of the sampled voltage 11 、U 21 (ii) a Inputting the third and fourth voltage values into the trained genetic optimization BP neural network A to obtain third and fourth predicted stable values U of the sampled voltage 12 、U 22 (ii) a Predicting a stable value U according to the first, the second, the third and the fourth 11 U 21 U 12 And U 22 Calculating the insulation resistance R of the anode and the cathode of the power battery p And R n Value of (A) and corresponding dielectric strength K p And K n The value of (c). The calculation principle is shown in fig. 4, and the calculation process is shown in the above formula (1) -formula (8), which is not described herein again.
In one implementation, the data processing module 44 further includes a fault determination unit, a fault level determination unit, and an equivalent capacitance value prediction unit.
In one implementation, the fault determining unit determines whether an insulation fault may occur according to the first and second voltage values transmitted from the signal acquiring module 43; in the event of a possible insulation fault condition, a fault determination signal a is provided to the control module 42.
The failure determination unit calculates the result R p And R n 、K p And K n And respectively comparing the values with the set insulation resistance value and the corresponding insulation strength value, and judging whether insulation faults occur again. Equivalent insulation resistance R between positive and negative poles of power battery and chassis ground p And R n Is less than a set value, it is determined that there is an insulation fault. The fault determination signal B is sent to the control module 42 in the case where it is determined that an insulation fault has occurred. And the secondary judgment of the insulation fault can avoid false alarm.
The fault grade determining unit is used for acquiring the arithmetic mean value of the positive and negative insulation strengths of the insulation faults occurring n times in unit time t under the condition that the insulation faults exist; and determining the grade of the insulation fault according to the arithmetic mean value of the insulation strength of the positive electrode and the negative electrode. The fault level signal is sent to the fault pre-warning module 45.
An equivalent capacitance value prediction unit for obtaining the equivalent insulation resistance R of the positive and negative electrodes with insulation failure for n times in unit time t p And R n An arithmetic mean of the resistance values; the value of n is a natural number; based on the equivalent insulation resistance R of the positive electrode and the negative electrode p And R n The arithmetic mean value of the resistance value and the sampling voltage value are obtained by carrying out inverse normalization processing on the output value in a trained second genetic optimization BP neural networkEquivalent Y capacitor C to positive and negative electrodes p And C n The predicted value of (2).
And the fault early warning module 45 is used for carrying out fault early warning according to the insulation fault.
In one possible embodiment, the fault pre-warning module 45 receives the power supplied by the power module 41, and starts the fault pre-warning function; according to the fault grade signal transmitted by the data processing module 44, different fault early warning operations are executed.
In one possible embodiment, the fault pre-warning module 45 is provided with an audible and visual alarm circuit including an insulated fault indicator light and a voice alarm.
Example 1
Fig. 7 is an insulation detection flowchart of the electric vehicle according to embodiment 1 of the present application. As shown in fig. 7, the insulation detection process of the electric vehicle provided in embodiment 1 of the present application includes the following steps S201 to S210.
S201, the insulation detection system sends a vehicle starting switch state signal to the power module 41 through the CAN bus.
S202, after receiving the start switch start signal, the power module 41 is turned on to provide the required electric energy for the normal operation of the control module 42, the signal acquisition module 43 and the data processing module 44.
At this time, the power module 41 does not supply power to the fault early warning module 45, and the fault early warning module 45 is in a shutdown state. When each module of the insulation detection system works normally, the system operation indicator lamp is normally on; when one or more modules of the system have faults in operation, the indicator light flashes.
S203, the signal acquisition module 43 sends the voltage data acquired by the sampling resistor to the data processing module 44. At the moment, the bridge switch is in a first state, and the voltage data are a first voltage value and a second voltage value.
S204, after the data processing module 44 receives the voltage data, comparing the voltage data, and when the voltage difference value of the positive and negative sampling resistors is smaller than a set value, indicating that the vehicle has no insulation fault, continuing to acquire the voltage data for comparison; when the voltage difference between the positive and negative sampling resistors is greater than the set value, it indicates that the vehicle may have an insulation fault, and at this time, a fault determination signal a is sent to the control module 42.
S205, the control module 42 sends a switch control command after receiving the failure determination signal a. And controlling the open-close state of a bridge switch in the detection circuit. The signal acquisition module 43 transmits the acquired voltage data to the data processing module 44. At this time, the bridge switch is in the second state, and the voltage data is the third and fourth voltage values.
S206, the data processing module 44 inputs the voltage data including the first, second, third and fourth values into the trained genetic optimization BP neural network A to obtain corresponding output values, and then performs inverse normalization processing on the output values to obtain the first, second, third and fourth predicted stable values U of the corresponding sampling voltages 11 、U 21 、U 12 、U 22 . Calculating the positive and negative insulation resistance R according to the predicted value of the neural network and the formulas (5) - (8) p And R n Value of (A) and corresponding dielectric strength K p And K n The value of (c). And comparing the calculation result with a set value, and judging whether the positive electrode and the negative electrode of the automobile power battery have insulation faults again. If the voltage is larger than the set value, indicating that the vehicle has no insulation fault, continuously acquiring voltage data for comparison; if the value is less than the set value, the vehicle is indicated to have an insulation fault, and a fault judgment signal B is sent to the control module 42 at the moment.
S207, after receiving the fault determination signal B, the control module 42 sends a power-on control instruction to the power module 41, and controls the power module 41 to supply power to the fault early warning module 45, so that the fault early warning module 45 is in a power-on state. At this time, the insulation fault indicator lamp of the fault early warning module 45 is not on, and the voice alarm is in a closed state. And the fault early warning module is electrified in advance, so that the response time of fault early warning is shortened.
S208, insulating strength K of insulating fault occurring n times in unit time t p And K n Respectively calculating the arithmetic mean value of K to obtain p And K n Is calculated as the arithmetic mean of (1). The average value is compared with a set value, the insulation fault level is judged, and a fault level signal is sent to the fault early warning module 45. Is provided with a first stageThe threshold value of the insulation fault is 1100 omega/V, the threshold value of the secondary insulation fault is 600 omega/V, namely when the insulation strength is more than 1100 omega/V, the vehicle has no insulation fault; when the voltage is more than 600 omega/V and less than 1100 omega/V, the voltage is a primary insulation fault; when the voltage is less than 600 omega/V, the voltage is a secondary insulation fault. It can be understood that, in case of a failure, the failure early warning module 45 is powered on; and continuously taking the average value for n times, and responding by the fault early warning module. Therefore, the response speed of the system can be improved, and the probability of false alarm can be reduced.
S209, the fault pre-warning module 45 receives the fault level signal from the data processing module 44, and executes different fault pre-warning operations according to the signal.
Illustratively, when the vehicle has no insulation fault, the insulation fault indicator lamp is not on, and the speech alarm is in a closed state; when a primary insulation fault occurs, the indicator lamp turns yellow and flickers at a lower frequency, and the voice alarm broadcasts the current fault level and warns people in the vehicle to pay attention to driving safety; when the second grade trouble appeared, then the pilot lamp turns red, and according to very fast frequency scintillation, voice alarm reports current fault level to personnel in the warning car in time overhauls.
S210, insulating the positive and negative electrodes with the resistor R p And R n The value and the sampling voltage data are input into a trained genetic optimization BP neural network B, and then the output value is subjected to inverse normalization processing to obtain a positive electrode equivalent Y capacitor C and a negative electrode equivalent Y capacitor C p And C n The predicted value of (2).
It will be further appreciated by those of ordinary skill in the art that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether these functions are performed in hardware or software depends on the particular application of the solution and design constraints. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments, objects, technical solutions and advantages of the present application are described in further detail, it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present application, and are not intended to limit the scope of the present application, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present application should be included in the scope of the present application.
Claims (7)
1. An insulation detection method of an electric automobile predicts equivalent insulation resistance (R) of a positive pole and a negative pole of a power battery to a chassis ground based on a bridge circuit connected between the positive pole and the negative pole of the power battery and the chassis ground p ) And (R) n ) Resistance value of (2) and equivalent Y capacitance (C) p ) And (C) n ) The bridge circuit comprises at least: a first resistor (R) 1 ) And a first switch (K) 1 ) A second resistor (R) connected between the positive electrode of the power battery and the chassis ground in parallel 2 ) And a second switch (K) 2 ) The method is connected between the negative pole of the power battery and the chassis ground in parallel, and is characterized by comprising the following steps:
at the first switch (K) 1 ) Open or close, second switch (K) 2 ) In the open or closed state, the first resistance (R) is obtained respectively 1 ) A second resistor (R) 2 ) A sampled voltage value of; the sampling voltage values comprise a first voltage value, a second voltage value, a third voltage value and a fourth voltage value;
obtaining first, second, third and fourth predicted stable values of the positive and negative sampling voltages according to the first, second, third and fourth voltage values and the trained first genetic optimization BP neural network;
determining the equivalent insulation resistance (R) of the positive pole and the negative pole of the power battery to the chassis ground according to the first, the second, the third and the fourth predicted stable values of the positive pole and the negative pole sampling voltages p ) And (R) n ) The resistance value of (1);
according to the equivalent insulation resistance (R) of the positive and negative electrodes p ) And (R) n ) Determining the positive and negative electrode equivalent Y capacitance (C) of the power battery through a trained second genetic optimization BP neural network p ) And (C) n ) The capacitance value of (a);
according to the equivalent insulation resistance (R) of the positive and negative electrodes p ) And (R) n ) Calculating the insulation strength values of the positive electrode and the negative electrode;
equivalent insulation resistance (R) between the positive and negative poles of the power battery and the chassis ground p ) And (R) n ) Determining that there is an insulation fault when one of the resistance values of (1) is less than a set value;
under the condition that the insulation fault exists, acquiring the arithmetic mean value of the insulation strength of the positive electrode and the negative electrode of the insulation fault occurring n times in unit time t;
and determining the grade of the insulation fault according to the arithmetic mean value of the insulation strength of the positive electrode and the insulation strength of the negative electrode.
2. The insulation detecting method for electric vehicle according to claim 1, wherein the first switch (K) is provided 1 ) Open or close, second switch (K) 2 ) In the open or closed state, the first resistance (R) is obtained respectively 1 ) A second resistor (R) 2 ) A sampled voltage value comprising:
at the first switch (K) 1 ) Closed and second switch (K) 2 ) In the off state, the first resistance (R) is obtained 1 ) A second resistor (R) 2 ) The corresponding first and second voltage values;
at the first switch (K) 1 ) Open and the second switch (K) 2 ) In the closed state, the first resistance (R) is obtained 1 ) A second resistor (R) 2 ) The third and fourth voltage values are corresponding;
wherein the first, second, third and fourth voltage values are at the equivalent Y capacitance (C) p ) And (C) n ) Under the influence of the first resistance (R) 1 ) A second resistor (R) 2 ) The sampled voltage of (c).
3. The method according to claim 1 or 2, characterized in that the method further comprises:
respectively acquiring the first resistances (R) 1 ) A second resistor (R) 2 ) The corresponding first and second voltage values;
comparing the difference between the first and second voltage values with a set value, and determining that an insulation fault exists if the difference is greater than the set value as a result of the comparison.
4. Method according to one of claims 1-2, characterized in that said equivalent insulation resistance (R) is determined according to said equivalent insulation resistance (R) p ) And (R) n ) Determining the equivalent Y capacitance (C) of the positive electrode and the negative electrode through a trained second genetic optimization BP neural network p ) And (C) n ) Comprises:
according to the equivalent insulation resistance (R) of the positive pole and the negative pole of the power battery to the chassis ground p ) And (R) n ) Comparing with a set value, and determining that an insulation fault exists;
under the condition that an insulation fault exists, acquiring the equivalent insulation resistance (R) of the positive electrode and the negative electrode of which the insulation fault occurs n times in unit time t p ) And (R) n ) An arithmetic mean of the resistance values; the value of n is a natural number;
based on the equivalent insulation resistance (R) of the positive electrode and the negative electrode p ) And (R) n ) The arithmetic mean value of the resistance value and the sampling voltage value are used for determining the equivalent Y capacitance (C) of the positive electrode and the negative electrode through a trained second genetic optimization BP neural network p ) And (C) n ) The capacitance value of (2).
5. Insulation detection system of electric automobile, based on positive and negative poles and chassis of power batteryThe bridge circuit connected between the grounds predicts the equivalent insulation resistance (R) of the positive pole and the negative pole of the power battery to the chassis ground p ) And (R) n ) Resistance value of (2) and equivalent Y capacitance (C) p ) And (C) n ) The bridge circuit comprises at least: a first resistor (R) 1 ) And a first switch (K) 1 ) A second resistor (R) connected between the positive electrode of the power battery and the chassis ground in parallel 2 ) And a second switch (K) 2 ) Connected in parallel between the negative pole of the power battery and the chassis ground, characterized in that the system at least comprises:
a signal acquisition module for switching on and off the first switch (K) 1 ) Open or close, second switch (K) 2 ) Respectively acquiring the first resistance (R) in an open or closed state 1 ) A second resistor (R) 2 ) A sampled voltage value of; the sampling voltage values comprise a first voltage value, a second voltage value, a third voltage value and a fourth voltage value;
the data processing module is used for obtaining first, second, third and fourth predicted stable values of the positive and negative sampling voltages according to the first, second, third and fourth voltage values and the trained first genetic optimization BP neural network; determining the equivalent insulation resistance (R) of the positive pole and the negative pole of the power battery to the chassis ground according to the first, the second, the third and the fourth predicted stable values of the positive pole and the negative pole sampling voltages p ) And (R) n ) The resistance value of (1); according to the equivalent insulation resistance (R) of the positive and negative electrodes p ) And (R) n ) Determining the positive and negative electrode equivalent Y capacitance (C) of the power battery through a trained second genetic optimization BP neural network p ) And (C) n ) The capacitance value of (a);
the data processing module further comprises a fault determining unit and a fault grade determining unit; a fault determination unit for acquiring the first resistances (R) respectively 1 ) A second resistor (R) 2 ) Comparing the difference value of the first voltage value and the second voltage value with a set value according to the corresponding first voltage value and second voltage value, and determining that an insulation fault exists under the condition that the difference value is greater than the set value according to the comparison result; or the positive pole and the negative pole of the power battery are opposite to the chassis groundEquivalent insulation resistance (R) p ) And (R) n ) Determining that there is an insulation fault when one of the resistance values of (1) is less than a set value; the fault grade determining unit is used for acquiring the arithmetic mean value of the positive and negative insulation strengths of the insulation faults occurring n times in unit time t under the condition that the insulation faults exist; determining the grade of the insulation fault according to the arithmetic mean value of the insulation strength of the positive electrode and the negative electrode;
a control module for controlling the first switch (K) 1 ) Open or close the second switch (K) 2 ) Open or closed state.
6. The system of claim 5, wherein the signal acquisition module further comprises:
a first sampling unit for sampling at the first switch (K) 1 ) Closed and second switch (K) 2 ) In the off state, the first resistance (R) is obtained 1 ) A second resistor (R) 2 ) The corresponding first and second voltage values;
a second sampling unit for sampling at the first switch (K) 1 ) Open and the second switch (K) 2 ) In the closed state, the first resistance (R) is obtained 1 ) A second resistor (R) 2 ) The third and fourth voltage values are corresponding;
wherein the first, second, third and fourth voltage values are at the equivalent Y capacitance (C) p ) And (C) n ) Under the influence of the first resistance (R) 1 ) A second resistor (R) 2 ) The sampled voltage of (c).
7. The system of claim 5 or 6, wherein the data processing module further comprises:
an insulation strength determining unit for determining the equivalent insulation resistance (R) of the positive and negative electrodes p ) And (R) n ) Determining the insulation strength values of the positive electrode and the negative electrode by the resistance value;
an equivalent capacitance value prediction unit for obtaining the equivalent insulation resistance (R) of the positive and negative electrodes with insulation failure occurring n times in unit time t under the condition that the insulation failure exists p ) And (R) n ) An arithmetic mean of the resistance values; the value of n is a natural number; based on the equivalent insulation resistance (R) of the positive electrode and the negative electrode p ) And (R) n ) The arithmetic mean value of the resistance value and the sampling voltage value are used for determining the equivalent Y capacitance (C) of the positive electrode and the negative electrode through a trained second genetic optimization BP neural network p ) And (C) n ) The capacitance value of (c).
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| CN115656905A (en) * | 2022-05-20 | 2023-01-31 | 深圳市道通合创新能源有限公司 | Failure diagnosis system, method and equipment for insulation detection function of electric vehicle |
| CN115114863A (en) * | 2022-08-23 | 2022-09-27 | 苏州清研精准汽车科技有限公司 | Battery pack Y capacitance prediction method and device, computer equipment and storage medium |
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| CN116953360B (en) * | 2023-09-20 | 2024-01-19 | 上海派能能源科技股份有限公司 | Insulation resistance rapid detection method of energy storage equipment |
| CN117665396B (en) * | 2024-01-31 | 2024-06-07 | 徐州徐工汽车制造有限公司 | Insulation resistance detection circuit, method, device and medium for power battery |
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