CN116953556A - Method, system, medium and equipment for online detection of multivariable redundant fault battery - Google Patents
Method, system, medium and equipment for online detection of multivariable redundant fault battery Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/52—Testing for short-circuits, leakage current or ground faults
Abstract
The application relates to the technical field of battery detection, and discloses a method, a system, a medium and equipment for online detection of a multivariable redundant fault battery, wherein the method comprises the following steps: acquiring terminal voltage and current in the running process of the battery pack, establishing a battery equivalent circuit model, carrying out on-line parameter identification on the battery pack to obtain ohmic internal resistance and polarized internal resistance of the battery pack, and acquiring the charge state of the battery pack; performing multivariate redundant detection by combining the battery equivalent circuit model, the polarized internal resistance, the ohmic internal resistance and the state of charge, and identifying a fault battery and the type of the fault battery in the battery pack; the system comprises an equivalent model module, a variable acquisition module and a detection module, wherein the medium comprises a computer program, the device comprises a memory, a processor and the computer program, and the computer program realizes the method when being executed by the processor. The application effectively detects and identifies the aged battery and the internal short-circuit battery in the battery pack.
Description
Technical Field
The application relates to the technical field of battery detection, in particular to a method, a system, a medium and equipment for online detection of a multivariable redundant fault battery.
Background
In recent years, the development of battery technology and battery management has promoted the wide application of lithium batteries in the fields of electric automobiles, energy storage and the like. However, thermal runaway accidents of lithium batteries frequently occur, and the thermal runaway accidents usually release a large amount of energy in a short time, so that serious financial loss and even casualties are caused, and the development of related industries of the lithium batteries is seriously restricted. One of the main causes of thermal runaway is intra-cell short circuit (Internal short circuit, ISC) caused by damage to the separator of the positive and negative electrodes of the cell. The internal short circuit is required to go through three stages from generation to final development to thermal runaway, when the internal short circuit is in the later stage, the voltage of the battery terminal is obviously reduced, the temperature is rapidly increased, the reserved safety time is extremely short, and the thermal runaway is extremely easy to be caused, so that the internal short circuit must be detected before the internal short circuit, namely before the internal short circuit in the middle stage.
However, the internal short circuit of the battery is not easily found in the early stage, and in order to find the internal short circuit in the early and middle stages before the internal short circuit, the internal short circuit detection is increasingly studied to avoid thermal runaway. Most of the current patent core ideas for detecting the internal short circuit are based on the assumption of consistency of single batteries among batteries, that is, whether the internal short circuit exists in the batteries is judged by consistency difference of characteristic parameters such as voltage or State of charge (SOC) of different single batteries in a serial battery pack at the same time, for example, a diagnosis method for the internal short circuit of the battery is disclosed (publication number CN 106802396B). However, when the internal short circuit is detected by a single variable, the selection of the threshold value is difficult, and if the selection of the threshold value is not proper, false alarm or the situation that the internal short circuit cannot be judged may occur. When the inconsistency among the single battery cells causes certain characteristic parameters (such as voltage, internal resistance, charge state and the like) to exceed a threshold value, the internal short circuit of the battery is determined. However, in the current internal short circuit detection method, internal short circuit detection is generally performed based on only one characteristic parameter, when internal short circuit detection is performed by only one characteristic parameter, the threshold value selection is more challenging, when the difference between the threshold value selection distance standard value is too large, internal short circuit early warning cannot be timely sent out, and when the threshold value selection distance standard value is too small, false positive phenomena caused by factors such as noise can be caused.
At the same time, the current manufacturing process does not completely erase the initial differences between the individual cells in the battery pack, and these small differences are negligible for new cells. However, in actual use, the temperature distribution in the battery pack is uneven, and the factors such as overcharge and overdischarge affect the battery pack, so that the performance decay rate of part of the battery cells is faster than that of other batteries, the consistency difference between the battery cells and the new battery is larger, and the battery cells have aging characteristics, so that the inconsistency among the single cells in the battery pack is increased. When consistency detection is performed on the battery pack containing the aged batteries, part of characteristic parameters also exceed a threshold value, and the aged batteries in the battery pack also generate abnormal voltage drops similar to internal short circuits and present the same result as the internal short circuits. Therefore, when a defective battery is detected using the consistency assumption, an internal short-circuited battery and an aged battery can be detected at the same time, but it cannot be confirmed that the defective battery is an internal short-circuited battery or an aged battery.
Disclosure of Invention
Therefore, the technical problem to be solved by the application is to overcome the defects in the prior art, and provide a method, a system, a medium and equipment for online detection of a multivariable redundant fault battery, which can effectively detect and identify an aged battery and an internally short-circuited battery in a battery pack.
In order to solve the technical problems, the application provides a method for online detection of a multivariable redundant fault battery, which comprises the following steps:
acquiring terminal voltage and current in the running process of the battery pack, establishing a battery equivalent circuit model, carrying out on-line parameter identification on the battery pack to obtain ohmic internal resistance and polarized internal resistance of the battery pack, and acquiring the charge state of the battery pack; and performing multivariate redundant detection by combining the battery equivalent circuit model, the polarized internal resistance, the ohmic internal resistance and the charge state, and identifying the fault battery and the fault battery type in the battery pack.
In one embodiment of the present application, the on-line parameter identification is performed on the battery pack to obtain the ohmic internal resistance and the polarization internal resistance of the battery pack, and the state of charge of the battery pack is obtained, which specifically includes:
and carrying out on-line parameter identification on the battery in the whole life cycle by using a recursive least square method with forgetting factors to obtain the characteristic parameters of the battery, namely ohmic internal resistance and polarized internal resistance, and obtaining the state of charge of the battery by an extended Kalman filtering algorithm on the basis.
In one embodiment of the application, the multi-variable redundancy detection is performed in combination with the battery equivalent circuit model, the polarized internal resistance, the ohmic internal resistance and the charge state, and comprises the following steps:
calculating the voltage correlation of adjacent batteries in the battery pack according to the voltages at two ends of each battery in the battery equivalent circuit model, setting a coefficient threshold value and identifying abnormal batteries by combining the voltage correlation;
setting a mutation threshold value for the abnormal battery identified according to the voltage correlation and judging whether the abnormal battery has internal short circuit or not by combining the change of the polarized internal resistance in the window period;
setting an ohmic internal resistance threshold value for an abnormal battery which is identified according to the change of the polarized internal resistance and does not have internal short circuit, and judging whether the battery is aged or not by combining the change of the ohmic internal resistance;
and setting a charge state threshold value for the battery which is identified to be not aged according to the change of the ohmic internal resistance, and judging whether the battery is internally short-circuited or not according to the charge state.
In one embodiment of the present application, according to the voltages at two ends of each battery in the battery equivalent circuit model, calculating the voltage correlation of adjacent batteries in the battery pack, setting a coefficient threshold and identifying abnormal batteries by combining the voltage correlation, specifically:
adjacent cells in the battery packxVoltage across and batteryyThe voltages at both ends are respectively used as variablesxSum variableyCalculating variables using correlation analysis methodsxSum variableyThe correlation between the two is used as a voltage correlation coefficient;
if the voltage correlation coefficient is smaller than a coefficient threshold value, the battery is judged to be an abnormal battery; and if the voltage correlation coefficient is greater than or equal to a coefficient threshold value, judging the battery as a normal battery.
In one embodiment of the present application, the correlation analysis method is a correlation coefficient method, and the calculation method of the voltage correlation coefficient of adjacent cells in the battery pack is as follows:
，
wherein ,r x,y representing the voltage dependence coefficients of adjacent cells within the stack,cov(x,y)representing variablesxAnd (3) withyIs used to determine the covariance of (1),、/>representing variables respectivelyxVariance, variable of (2)yIs a variance of (c).
In one embodiment of the present application, the step of setting a mutation threshold and determining whether an internal short circuit occurs in the abnormal battery by combining the change of the polarization internal resistance in the window period specifically includes:
calculating the variation amplitude of the average value of the polarized internal resistance in the window period compared with the average value of the historical polarized internal resistance in the same charge state interval,
if the change amplitude of the average value of the polarized internal resistance in the window period is more than the abrupt threshold value compared with the average value of the historical polarized internal resistance in the same charge state interval, the battery is internally short-circuited, and the internal short circuit is already developed to the middle and later stages; if the abrupt threshold is not exceeded, the battery is an aged battery, a normal battery with a large measurement error, or a battery in an early internal short circuit.
In one embodiment of the present application, the setting the ohmic internal resistance threshold and the determining whether the battery is aged by combining the change of the ohmic internal resistance specifically includes:
calculating the deviation degree of the average value of the ohmic internal resistance of the abnormal battery without internal short circuit in the battery pack compared with the average value of the ohmic internal resistance of the normal battery,
if the deviation degree of the average value of the ohmic internal resistances of the abnormal batteries without internal short circuit in the battery pack compared with the average value of the ohmic internal resistances of the normal batteries exceeds an ohmic internal resistance threshold value, the batteries are aged batteries, and the batteries are discharged out of the consistency comparison sequence; if the ohmic internal resistance threshold is not exceeded, the battery is in an early stage of internal short circuit or is a normal battery with larger measurement error.
In one embodiment of the present application, the setting a state of charge threshold and determining whether an internal short circuit occurs in the battery in combination with the state of charge specifically includes:
the degree of deviation of the average value of the states of charge of the cells in the battery pack in which no aging occurs from the average value of the states of charge of the normal cells is calculated,
if the deviation degree of the average value of the charge states of the batteries without aging in the battery pack compared with the average value of the charge states of the normal batteries exceeds a charge state threshold value after a preset time period, the batteries are internally short-circuited; if the degree of deviation of the average value of the states of charge of the cells in which no aging has occurred within the battery pack from the average value of the states of charge of the normal cells does not change for a preset period of time and remains in the vicinity of 0, the cells are normal cells.
In one embodiment of the application, the coefficient threshold is 0.5, the abrupt threshold is 100%, the ohmic internal resistance threshold is 10%, and the state of charge threshold is ±5%.
The application also provides a system for online detection of the multivariable redundant fault battery, which comprises:
the equivalent model module is used for acquiring terminal voltage and current in the running process of the battery pack to establish a battery equivalent circuit model;
the variable acquisition module is used for carrying out on-line parameter identification on the battery pack to obtain the ohmic internal resistance and the polarized internal resistance of the battery pack and acquire the charge state of the battery pack;
the detection module is used for carrying out multivariate redundancy detection by combining the battery equivalent circuit model, the polarized internal resistance, the ohmic internal resistance and the state of charge, and identifying the fault battery and the fault battery type in the battery pack.
The application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method of online detection of a multi-variable redundant faulty battery.
The application also provides a device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of multi-variable redundant fault cell on-line detection when executing the computer program.
Compared with the prior art, the technical scheme of the application has the following advantages:
according to the application, the battery equivalent circuit model is established, and the multi-variable redundancy detection is carried out by combining the polarization internal resistance, the ohmic internal resistance and the charge state of the battery on the basis, so that the possibility of false alarm during single variable detection is reduced, and the accuracy of fault battery detection is effectively improved; the type of the fault battery can be effectively and simply identified, and the effective distinction between the aged battery and the internal short-circuit battery is realized; in addition, the detection time of short circuit in the middle and later stages can be reduced, and thermal runaway can be effectively prevented.
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In order that the application may be more readily understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings, in which:
fig. 1 is a flow chart of the method of the present application.
Fig. 2 is a step diagram of the present application.
Fig. 3 is a Thevenin model of the circuit built in the present application.
Fig. 4 is a schematic diagram of a battery pack established at the time of performing an experiment in an embodiment of the present application.
Fig. 5 is a schematic diagram of current excitation loading a battery pack when performing experiments in an embodiment of the present application.
FIG. 6 shows the setting of equivalent internal short-circuit resistance in experiments conducted in accordance with an embodiment of the present applicationR isc Result plot when =100Ω.
FIG. 7 shows the setting of equivalent internal short-circuit resistance during experiments in accordance with an embodiment of the present applicationR isc Result plot when =1Ω.
Detailed Description
The present application will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the application and practice it.
Example 1
Referring to fig. 1-2, the application discloses a method for online detection of a multivariable redundant fault battery, which comprises the following steps:
s1: and acquiring terminal voltage and current in the running process of the battery pack to establish a battery equivalent circuit model, wherein the battery equivalent circuit model can be a Rint model, a Thevenin model, a PNGV model, a DP model and the like, the terminal voltage and the current in the running process of the battery pack are used in the embodiment, and the established model is shown in figure 3.
S2: and carrying out on-line parameter identification on the battery pack in the whole life cycle to obtain the ohmic internal resistance and the polarized internal resistance of the battery pack, and obtaining the State of Charge (SOC) of the battery pack.
In this embodiment, a recursive least square method with forgetting factors is used to perform online parameter identification on the battery in the whole life cycle to obtain the ohmic internal resistance of the battery characteristic parameterR 0 And internal resistance to polarizationR p And obtaining the state of charge of the battery by an extended Kalman filtering algorithm on the basis.
S3: and performing multivariate redundant detection by combining the battery equivalent circuit model, the polarized internal resistance, the ohmic internal resistance and the charge state, and identifying the fault battery and the fault battery type in the battery pack.
S3-1: and calculating the voltage correlation of adjacent batteries in the battery pack according to the voltages at the two ends of each battery in the battery equivalent circuit model, setting a coefficient threshold value and identifying abnormal batteries by combining the voltage correlation. The method comprises the following steps:
adjacent cells in the battery packxVoltage across and batteryyThe voltages at both ends are respectively used as variablesxSum variableyCalculating variables using correlation analysis methodsxSum variableyThe correlation coefficient between them is used as the voltage correlation coefficient. The correlation analysis method can be a graph correlation analysis method, a covariance and covariance matrix method, a correlation coefficient method, a unitary regression and multiple regression method, an information entropy and mutual information method and the like.
The correlation analysis method used in the embodiment is a correlation coefficient method, and the calculation method of the voltage correlation coefficient of the adjacent cells in the battery pack is as follows:
（1），
wherein ,r x,y representing the voltage dependence coefficients of adjacent cells within the stack,cov(x,y)representing variablesxAnd (3) withyIs used to determine the covariance of (1),、/>representing variables respectivelyxVariance, variable of (2)yIs a variance of (2);x i representing the ith variablex，y i Representing the ith variabley；/>、/>Representing variables respectivelyxMean, variable of (2)yIs used for the average value of (a),nrepresenting variablesxSum variableyIs a data amount of (a) in the data stream.r x,y The value range of (C) is [ -1,1]When (when)r x,y When=1, the variables are representedxAnd variable(s)yThe complete positive correlation is achieved,r x,y =0 represents a variablexAnd variable(s)yIs totally irrelevant, butr x,y = -1 represents a variablexAnd variable(s)yThe complete negative correlation. Ideally, the voltage correlation coefficient of two normal working series batteries is 1, and when the voltage drops abnormally, the voltage correlation coefficient of the two batteries will drop.
If the voltage correlation coefficient is smaller than a coefficient threshold value, the battery is judged to be an abnormal battery; and if the voltage correlation coefficient is greater than or equal to a coefficient threshold value, judging the battery as a normal battery. Coefficient threshold in this embodimentθ 1 Set to 0.5, if a certain batteryAnd if the correlation coefficient of the two voltages is lower than the threshold value, judging that the battery is an abnormal battery, and if the correlation coefficient is higher than or equal to the threshold value, judging that the battery is a normal battery. When detecting abnormal cells using the "voltage correlation coefficient" method, a cell with poor voltage consistency is identified, which may be an internal short-circuited cell, an aged cell, or a normal cell with a large measurement voltage error due to interference of factors such as noise.
S3-2: and setting a mutation threshold value for the abnormal battery identified according to the voltage correlation, and judging whether the abnormal battery has internal short circuit or not by combining the change of the polarized internal resistance in the window period. In this embodiment, the window period is 50s after the voltage correlation coefficient is smaller than the set coefficient threshold. The method comprises the following steps:
calculating the variation amplitude of the average value of the polarized internal resistance in the window period compared with the average value of the historical polarized internal resistance in the same charge state interval:
（2），
wherein ,ΔR p For polarization internal resistanceR p The average value of (2) is compared with the variation amplitude of the average value of the historical polarization internal resistance in the same charge state interval;for polarization internal resistanceR p Mean value during window in 50s after internal short trigger; />When the current battery is not abnormal, the historical average value of the internal polarization resistance in the charge state interval which is the same as the window period is obtained.
If the change amplitude of the average value of the polarized internal resistances in the window period is more than the set abrupt threshold value compared with the average value of the historical polarized internal resistances in the same charge state interval, the abnormal battery is considered to have an internal short circuit, and the internal short circuit is already developed to the middle and later stages; if the abrupt threshold is not exceeded, the abnormal cell at this time is considered to be an aged cell, a normal cell with a large measurement error, or an early internal short circuit. The book is provided withIn the examples the mutation thresholdθ 2 Set to 100%.
S3-3: and setting an ohmic internal resistance threshold value for the abnormal battery which is identified according to the change of the polarized internal resistance and does not have internal short circuit, and judging whether the battery is aged or not by combining the change of the ohmic internal resistance. The method comprises the following steps:
calculating the deviation degree of the average value of the ohmic internal resistances of the abnormal batteries without internal short circuit in the battery pack compared with the average value of the ohmic internal resistances of the normal batteries:
（3），
wherein ,ΔR i0, Indicating that no internal short circuit has occurred in the battery packiOhmic internal resistance of abnormal cellR 0 A degree of deviation of the average value of the ohmic internal resistance of the normal battery compared to the average value of the ohmic internal resistance of the normal battery;R i0, represent the firstiOhmic internal resistance of the abnormal cell;R j0,abn, represent the firstjOhmic internal resistance of the abnormal cell;nindicating the total number of cells in the series stack,krepresenting the total number of abnormal cells within the series battery pack;
if the deviation degree of the average value of the ohmic internal resistances of the abnormal batteries without internal short circuit in the battery pack compared with the average value of the ohmic internal resistances of the normal batteries exceeds an ohmic internal resistance threshold value, the batteries are considered to be aged batteries, and the batteries are removed from the consistency comparison sequence; if the ohmic internal resistance threshold is not exceeded, the battery is in an early stage of internal short circuit or is a normal battery with larger measurement error. The Euramer internal resistance threshold in the present embodimentθ 3 Set to 10%.
S3-4: and setting a charge state threshold value for the battery which is identified to be not aged according to the change of the ohmic internal resistance, and judging whether the battery is internally short-circuited or not according to the charge state. The method comprises the following steps:
calculating the deviation degree of the average value of the charge states of the batteries without aging in the battery pack compared with the average value of the charge states of the normal batteries:
（4），
wherein delta isSOC i Indicating that no aging has occurred in the battery packiThe degree of deviation of the average value of the state of charge of the abnormal battery compared with the average value of the state of charge of the normal battery;SOC i represent the firstiThe state of charge of the battery cell is controlled,SOC jabn, represent the firstjThe state of charge of the abnormal battery is saved;nindicating the total number of cells in the series stack,kindicating the total number of abnormal cells in the series stack.
If the deviation degree of the average value of the charge states of the batteries without aging in the battery pack compared with the average value of the charge states of the normal batteries exceeds a charge state threshold value after a preset time period, the batteries are considered to have internal short circuits; if the average value of the states of charge of the cells in the battery pack, in which no aging has occurred, does not change over a preset period of time and remains near 0 as compared to the average value of the states of charge of the normal cells, the cells are considered to have false positives, and are normal cells affected by measurement errors. In this embodiment, the preset time period is determined according to the actual situation, and the threshold value of the state of charge is presetθ 4 Setting to + -5%.
Example two
The application also discloses an online detection system of the multivariable redundant fault battery, which comprises the following steps: the equivalent model module is used for acquiring terminal voltage and current in the running process of the battery pack to establish a battery equivalent circuit model; the variable acquisition module is used for carrying out on-line parameter identification on the battery pack to obtain the ohmic internal resistance and the polarized internal resistance of the battery pack and acquire the charge state of the battery pack; the detection module is used for carrying out multivariate redundancy detection by combining the battery equivalent circuit model, the polarized internal resistance, the ohmic internal resistance and the state of charge, and identifying the fault battery and the fault battery type in the battery pack.
Example III
The application also discloses a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the method for online detection of a multivariable redundant faulty battery in the first embodiment.
Example IV
The application also discloses a device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for online detection of the multivariable redundant fault battery in the first embodiment when executing the computer program.
Compared with the prior art, the application has the advantages that:
(1) According to the application, the battery equivalent circuit model is established, and the multi-variable redundancy detection is carried out by combining the polarized internal resistance, the ohmic internal resistance and the charge state of the battery on the basis, so that the problem that the threshold value is difficult to select when the fault battery is detected only through a single characteristic variable is solved, the independent threshold value is conveniently set for each variable, and the detection is simple and convenient.
(2) The application reduces the possibility of false alarm when single variable is detected, and effectively improves the accuracy of fault battery detection.
(3) The application solves the problem that the conventional consistency detection method cannot distinguish the fault types of the aged battery and the internal short-circuit battery, can effectively and simply identify the fault type, and realizes the effective distinction of the aged battery and the internal short-circuit battery.
(4) The application can effectively detect and identify the early internal short circuit, reduce the detection time of the short circuit in the middle and later stages and effectively prevent thermal runaway.
In order to further verify the beneficial effects of the application, 6 batteries Cell-1 and Cell-2 … Cell-6 are selected for experiments in the embodiment. Where Cell-5 is an aged battery whose State of Health (soh=92%). As shown in FIG. 4, resistors with different resistance values are sequentially connected in parallel at two ends of the Cell-2R isc (100 omega, 1 omega) is used for simulating internal short circuits with different degrees, and the start and stop of the internal short circuits are controlled through a switch. The same current excitation as in fig. 5 is applied to charge and discharge the series battery pack. When the experiment was carried out for 720s, the switch was closedS 1 Control Cell-2 to trigger internal short circuit and dieAnd (5) simulating the situation that the normal battery in the battery pack is internally short-circuited. When the voltage of a certain battery in the battery pack is lower than 2.5V, stopping the experiment and opening the switchSAnd stopping internal short circuit to prevent overdischarge.
For equivalent internal short-circuit resistanceR isc For early internal short circuit of 100 Ω, the specific experimental results are shown in fig. 6, where the dashed straight line is the threshold value of each characteristic parameter. The flow of performing the multivariate redundancy check is as follows:
first, the voltage correlation coefficients are comparedr x,y The results are shown in the first graph of fig. 6. When the time is 347s and 736s, respectively, the first detectionr 4,5 、r 5,6 And (3) withr 1,2 、r 2,3 If the voltage correlation coefficient of (a) exceeds the threshold value, the batteries Cell-5 and Cell-2 can be calibrated to be abnormal batteries, but the abnormal batteries cannot be judged to be internal short-circuit batteries, aged batteries or normal batteries with larger measurement noise;
secondly, comparing the average value change amplitude delta of the polarized internal resistance in the window period after the correlation coefficient exceeds the threshold value momentR p The results are shown in the second graph of fig. 6. The average value change amplitude of the polarization internal resistances of the abnormal batteries Cell-5 and Cell-2 is lower than 100%, so that whether the abnormal batteries have internal short circuit or not cannot be determined;
then, the deviation degree delta of the ohmic internal resistance is comparedR i0, The results are shown in the third graph of fig. 6. Judging the Cell-5 as an aged battery when the ohm internal resistance deviation degree of the Cell-5 exceeds a threshold value, and removing the Cell-5 from the comparison sequence of the deviation degree of the charge state;
finally, the degree of deviation delta of the charge states of the remaining abnormal battery Cell-2 and other batteries except the aged battery is comparedSOC i The results are shown in the fourth graph of fig. 6. The state of charge deviation degree of other normal batteries can be always kept near 0, the state of charge deviation degree of the Cell-2 exceeds a threshold value after the first abnormal early warning, and the Cell-2 is judged to have an internal short circuit and is in an early stage of the internal short circuit because the mean value deviation degree of the polarization internal resistance of the Cell-2 is lower than the threshold value.
Similarly, the method is used for equivalent internal short circuit resistanceR isc In the case of 1 Ω, the changes in the characteristic parameters are shown in fig. 7. And (3) withR isc When it is 100deg.C, it is different for Cell-2, whenR isc When the average value is 1 omega, the average value change amplitude delta of the polarization internal resistance is within a 50s window period after the first abnormal early warningR p 586% respectively, exceeding the threshold value by 100%, cell-2 can be determined as an internal short-circuit battery; in the case of Cell-5, the variation range is less than 100%, and it is impossible to determine whether or not the Cell has an internal short circuit. Therefore, the battery Cell-2 can be directly judged to be in an internal short circuit state and possibly in the middle and late stages of the internal short circuit, so that delta is not needed when the application is practically applied to detect the battery faultSOC i And (5) performing comparative analysis. In this experiment, delta was still measured in order to compare the detection times of the two detection methodsSOC i Analysis was performed. As can be seen from the fourth graph in FIG. 7, whenR isc When the ratio is 1Ω, use ΔSOC i The time required for detecting the internal short circuit is 392 s respectively, and the 50s window period required by the internal short circuit is identified by the far-passing polarization internal resistance. At the same time due to thisR isc Smaller, deeper internal short circuit, less internal short circuit detection time margin, if delta is usedSOC i The internal short circuit detection is carried out, and the thermal runaway risk possibly exists, so the method reduces the internal short circuit detection time in the middle and later stages and reduces the thermal runaway risk.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present application will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present application.
Claims (12)
1. A method for online detection of a multi-variable redundant fault battery, comprising:
acquiring terminal voltage and current in the running process of the battery pack, establishing a battery equivalent circuit model, carrying out on-line parameter identification on the battery pack to obtain ohmic internal resistance and polarized internal resistance of the battery pack, and acquiring the charge state of the battery pack; and performing multivariate redundant detection by combining the battery equivalent circuit model, the polarized internal resistance, the ohmic internal resistance and the charge state, and identifying the fault battery and the fault battery type in the battery pack.
2. The method for online detection of a multi-variable redundant fault battery according to claim 1, wherein: the method comprises the steps of carrying out on-line parameter identification on the battery pack to obtain ohmic internal resistance and polarized internal resistance of the battery pack, and obtaining the charge state of the battery pack, wherein the specific steps are as follows:
and carrying out on-line parameter identification on the battery in the whole life cycle by using a recursive least square method with forgetting factors to obtain the characteristic parameters of the battery, namely ohmic internal resistance and polarized internal resistance, and obtaining the state of charge of the battery by an extended Kalman filtering algorithm on the basis.
3. The method for online detection of a multi-variable redundant fault battery according to claim 1, wherein: and combining the battery equivalent circuit model, the polarized internal resistance, the ohmic internal resistance and the charge state to perform multivariable redundancy detection, wherein the method comprises the following steps of:
calculating the voltage correlation of adjacent batteries in the battery pack according to the voltages at two ends of each battery in the battery equivalent circuit model, setting a coefficient threshold value and identifying abnormal batteries by combining the voltage correlation;
setting a mutation threshold value for the abnormal battery identified according to the voltage correlation and judging whether the abnormal battery has internal short circuit or not by combining the change of the polarized internal resistance in the window period;
setting an ohmic internal resistance threshold value for an abnormal battery which is identified according to the change of the polarized internal resistance and does not have internal short circuit, and judging whether the battery is aged or not by combining the change of the ohmic internal resistance;
and setting a charge state threshold value for the battery which is identified to be not aged according to the change of the ohmic internal resistance, and judging whether the battery is internally short-circuited or not according to the charge state.
4. The method for online detection of a multi-variable redundant fault battery of claim 3, wherein: calculating the voltage correlation of adjacent batteries in the battery pack according to the voltages at two ends of each battery in the battery equivalent circuit model, setting a coefficient threshold value, and identifying abnormal batteries by combining the voltage correlation, wherein the method specifically comprises the following steps:
adjacent cells in the battery packxVoltage across and batteryyThe voltages at both ends are respectively used as variablesxSum variableyCalculating variables using correlation analysis methodsxSum variableyThe correlation between the two is used as a voltage correlation coefficient;
if the voltage correlation coefficient is smaller than a coefficient threshold value, the battery is judged to be an abnormal battery; and if the voltage correlation coefficient is greater than or equal to a coefficient threshold value, judging the battery as a normal battery.
5. The method for online detection of a multi-variable redundant fault battery according to claim 4, wherein: the correlation analysis method is a correlation coefficient method, and the calculation method of the voltage correlation coefficient of adjacent batteries in the battery pack is as follows:
，
wherein,r x,y representing the voltage dependence coefficients of adjacent cells within the stack,cov(x,y)representing variablesxAnd (3) withyIs used to determine the covariance of (1),、/>representing variables respectivelyxVariance, variable of (2)yIs a variance of (c).
6. The method for online detection of a multi-variable redundant fault battery of claim 3, wherein: the method for judging whether the abnormal battery is internally short-circuited or not by setting the mutation threshold and combining the change of the polarization internal resistance in the window period comprises the following steps:
calculating the variation amplitude of the average value of the polarized internal resistance in the window period compared with the average value of the historical polarized internal resistance in the same charge state interval,
if the change amplitude of the average value of the polarized internal resistance in the window period is more than the abrupt threshold value compared with the average value of the historical polarized internal resistance in the same charge state interval, the battery is internally short-circuited, and the internal short circuit is already developed to the middle and later stages; if the abrupt threshold is not exceeded, the battery is an aged battery, a normal battery with a large measurement error, or a battery in an early internal short circuit.
7. The method for online detection of a multi-variable redundant fault battery of claim 3, wherein: the method for judging whether the battery is aged or not by setting the ohmic internal resistance threshold and combining the change of the ohmic internal resistance comprises the following steps:
calculating the deviation degree of the average value of the ohmic internal resistance of the abnormal battery without internal short circuit in the battery pack compared with the average value of the ohmic internal resistance of the normal battery,
if the deviation degree of the average value of the ohmic internal resistances of the abnormal batteries without internal short circuit in the battery pack compared with the average value of the ohmic internal resistances of the normal batteries exceeds an ohmic internal resistance threshold value, the batteries are aged batteries, and the batteries are discharged out of the consistency comparison sequence; if the ohmic internal resistance threshold is not exceeded, the battery is in an early stage of internal short circuit or is a normal battery with larger measurement error.
8. The method for online detection of a multi-variable redundant fault battery of claim 3, wherein: the method comprises the steps of setting a state of charge threshold and judging whether the battery is internally short-circuited according to the state of charge, wherein the method comprises the following specific steps:
the degree of deviation of the average value of the states of charge of the cells in the battery pack in which no aging occurs from the average value of the states of charge of the normal cells is calculated,
if the deviation degree of the average value of the charge states of the batteries without aging in the battery pack compared with the average value of the charge states of the normal batteries exceeds a charge state threshold value after a preset time period, the batteries are internally short-circuited; if the degree of deviation of the average value of the states of charge of the cells in which no aging has occurred within the battery pack from the average value of the states of charge of the normal cells does not change for a preset period of time and remains in the vicinity of 0, the cells are normal cells.
9. The method for on-line detection of a multi-variable redundant fault battery according to any one of claims 3-8 wherein: the coefficient threshold is 0.5, the abrupt threshold is 100%, the ohmic internal resistance threshold is 10%, and the state of charge threshold is + -5%.
10. A system for online detection of a multi-variable redundant faulty battery, comprising:
the equivalent model module is used for acquiring terminal voltage and current in the running process of the battery pack to establish a battery equivalent circuit model;
the variable acquisition module is used for carrying out on-line parameter identification on the battery pack to obtain the ohmic internal resistance and the polarized internal resistance of the battery pack and acquire the charge state of the battery pack;
the detection module is used for carrying out multivariate redundancy detection by combining the battery equivalent circuit model, the polarized internal resistance, the ohmic internal resistance and the state of charge, and identifying the fault battery and the fault battery type in the battery pack.
11. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements a method of on-line detection of a multi-variable redundant faulty battery according to any one of claims 1-9.
12. An apparatus, characterized in that: a computer program comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing a method of on-line detection of a multi-variable redundant faulty battery according to any one of claims 1 to 9 when said computer program is executed.
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