CN116256661B - Battery fault detection method, device, electronic equipment and storage medium - Google Patents

Battery fault detection method, device, electronic equipment and storage medium Download PDF

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Publication number
CN116256661B
CN116256661B CN202310548951.1A CN202310548951A CN116256661B CN 116256661 B CN116256661 B CN 116256661B CN 202310548951 A CN202310548951 A CN 202310548951A CN 116256661 B CN116256661 B CN 116256661B
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fault
detected
determining
factor
comparison result
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CN116256661A (en
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赵珈卉
朱勇
张斌
刘明义
王建星
刘承皓
孙悦
郝晓伟
平小凡
成前
杨超然
白盼星
王娅宁
周敬伦
段召容
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Huaneng Clean Energy Research Institute
Huaneng Lancang River Hydropower Co Ltd
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Huaneng Clean Energy Research Institute
Huaneng Lancang River Hydropower Co Ltd
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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]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • 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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Secondary Cells (AREA)

Abstract

The disclosure provides a battery fault detection method, a device, an electronic device and a storage medium, wherein the method comprises the following steps: the method comprises the steps of obtaining an equivalent circuit model of a single battery to be detected, identifying an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value in the equivalent circuit model, calculating a first abnormal factor corresponding to the ohmic internal resistance value, a second abnormal factor corresponding to the polarization capacitance value and a third abnormal factor corresponding to the polarization resistance value, calculating a first fault index corresponding to the first abnormal factor, a second fault index corresponding to the second abnormal factor and a third fault index corresponding to the third abnormal factor, and determining a fault detection result corresponding to the single battery to be detected based on the first fault index, the second fault index and the third fault index, so that the accuracy and the applicability of battery fault detection can be effectively improved, the reliability and the safety of an energy storage battery system can be improved, and meanwhile, the maintenance cost and the time are reduced.

Description

Battery fault detection method, device, electronic equipment and storage medium
Technical Field
The disclosure relates to the technical field of intelligent fault diagnosis of battery energy storage systems, in particular to a battery fault detection method, a device, electronic equipment and a storage medium.
Background
With the rapid development of new energy technology, energy storage battery systems have become an important component in renewable energy sources and smart grids. And the performance and life of the energy storage battery directly affect the efficiency and stability of the energy storage system. In energy storage battery systems, battery voltage is one of the important parameters that may reflect the state and performance of the battery. However, since the energy storage battery is affected by various factors such as temperature, charge and discharge cycles, use time, etc., there is a tendency for the battery voltage to have a certain fluctuation and instability. In the actual use process, when the voltage of the energy storage battery changes abnormally, faults and accidents of the energy storage system can be caused. Therefore, there is a need for a method of detecting a voltage failure of an energy storage battery based on abnormal point detection.
In the related art, when detecting a battery failure based on an abnormal point, only a single abnormal value is generally detected, and it is difficult to detect a plurality of failures.
In this way, the applicability of the detection process and the accuracy of the detection result cannot be ensured.
Disclosure of Invention
The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art.
Therefore, an object of the present disclosure is to provide a method, an apparatus, an electronic device, and a storage medium for detecting a battery fault, which can effectively improve the accuracy and applicability of detecting a battery fault, improve the reliability and safety of an energy storage battery system, and reduce maintenance cost and time.
The battery fault detection method provided by the embodiment of the first aspect of the present disclosure includes:
obtaining an equivalent circuit model of a single battery to be detected, wherein the single battery to be detected belongs to a battery pack to be detected, and the battery pack to be detected comprises a plurality of single batteries to be detected;
identifying model parameters in the equivalent circuit model, wherein the model parameters comprise an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value;
calculating a target abnormality factor corresponding to the model parameter, wherein the target abnormality factor comprises: a first anomaly factor corresponding to the ohmic internal resistance value, a second anomaly factor corresponding to the polarization capacitance value, and a third anomaly factor corresponding to the polarization resistance value;
calculating a target fault index corresponding to the target abnormal factor, wherein the target fault index comprises: a first fault indicator corresponding to the first anomaly factor, a second fault indicator corresponding to the second anomaly factor, and a third fault indicator corresponding to the third anomaly factor;
And determining a fault detection result corresponding to the single battery to be detected based on the first fault index, the second fault index and the third fault index.
According to the battery fault detection method provided by the embodiment of the first aspect of the present disclosure, an equivalent circuit model of a to-be-detected single battery is obtained, wherein the to-be-detected single battery belongs to a to-be-detected battery pack, the to-be-detected battery pack includes a plurality of to-be-detected single batteries, model parameters in the equivalent circuit model are identified, the model parameters include an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value, and a target abnormal factor corresponding to the model parameters is calculated, wherein the target abnormal factor includes: the method comprises the steps of calculating a target fault index corresponding to a target abnormal factor by a first abnormal factor corresponding to an ohmic internal resistance value, a second abnormal factor corresponding to a polarization capacitance value and a third abnormal factor corresponding to a polarization resistance value, wherein the target fault index comprises: the method comprises the steps of determining a fault detection result corresponding to a single battery to be detected based on a first fault index corresponding to a first abnormal factor, a second fault index corresponding to a second abnormal factor and a third fault index corresponding to a third abnormal factor, thereby effectively improving the accuracy and applicability of battery fault detection, improving the reliability and safety of an energy storage battery system, and reducing maintenance cost and time.
The battery fault detection device provided by the embodiment of the second aspect of the present disclosure includes:
the device comprises an acquisition module, a detection module and a detection module, wherein the acquisition module is used for acquiring an equivalent circuit model of a single battery to be detected, the single battery to be detected belongs to a battery pack to be detected, and the battery pack to be detected comprises a plurality of single batteries to be detected;
the identification module is used for identifying model parameters in the equivalent circuit model, wherein the model parameters comprise an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value;
the first calculation module is used for calculating a target abnormal factor corresponding to the model parameter, wherein the target abnormal factor comprises: a first anomaly factor corresponding to the ohmic internal resistance value, a second anomaly factor corresponding to the polarization capacitance value, and a third anomaly factor corresponding to the polarization resistance value;
the second calculation module is configured to calculate a target fault indicator corresponding to the target abnormality factor, where the target fault indicator includes: a first fault indicator corresponding to the first anomaly factor, a second fault indicator corresponding to the second anomaly factor, and a third fault indicator corresponding to the third anomaly factor;
And the determining module is used for determining a fault detection result corresponding to the single battery to be detected based on the first fault index, the second fault index and the third fault index.
According to the battery fault detection device provided by the embodiment of the second aspect of the disclosure, an equivalent circuit model of a single battery to be detected is obtained, wherein the single battery to be detected belongs to a battery pack to be detected, the battery pack to be detected comprises a plurality of single batteries to be detected, model parameters in the equivalent circuit model are identified, the model parameters comprise an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value, and a target abnormal factor corresponding to the model parameters is calculated, wherein the target abnormal factor comprises: the method comprises the steps of calculating a target fault index corresponding to a target abnormal factor by a first abnormal factor corresponding to an ohmic internal resistance value, a second abnormal factor corresponding to a polarization capacitance value and a third abnormal factor corresponding to a polarization resistance value, wherein the target fault index comprises: the method comprises the steps of determining a fault detection result corresponding to a single battery to be detected based on a first fault index corresponding to a first abnormal factor, a second fault index corresponding to a second abnormal factor and a third fault index corresponding to a third abnormal factor, thereby effectively improving the accuracy and applicability of battery fault detection, improving the reliability and safety of an energy storage battery system, and reducing maintenance cost and time.
An electronic device according to an embodiment of a third aspect of the present disclosure includes: a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed implements a battery fault detection method as set forth in an embodiment of the first aspect of the present disclosure.
An embodiment of a fourth aspect of the present disclosure proposes a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements a battery fault detection method as proposed by an embodiment of the first aspect of the present disclosure.
Embodiments of a fifth aspect of the present disclosure propose a computer program product, which when executed by a processor, performs a battery fault detection method as proposed by embodiments of the first aspect of the present disclosure.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a battery fault detection method according to an embodiment of the present disclosure;
Fig. 2 is a flow chart illustrating a battery fault detection method according to another embodiment of the present disclosure;
fig. 3 is a flowchart illustrating a battery fault detection method according to another embodiment of the present disclosure;
FIG. 4 is a flow chart of a fault detection method using local outliers according to the present disclosure;
fig. 5 is a schematic structural diagram of a battery fault detection device according to an embodiment of the present disclosure;
fig. 6 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present disclosure and are not to be construed as limiting the present disclosure. On the contrary, the embodiments of the disclosure include all alternatives, modifications, and equivalents as may be included within the spirit and scope of the appended claims.
Fig. 1 is a flowchart of a battery fault detection method according to an embodiment of the present disclosure.
It should be noted that, the main execution body of the battery fault detection method in this embodiment is a battery fault detection device, and the device may be implemented in a software and/or hardware manner, and the device may be configured in an electronic device, where the electronic device may include, but is not limited to, a terminal, a server, and the like, and the terminal may be, for example, a mobile phone, a palm computer, and the like.
As shown in fig. 1, the battery fault detection method includes:
s101: and obtaining an equivalent circuit model of the single battery to be detected, wherein the single battery to be detected belongs to a battery pack to be detected, and the battery pack to be detected comprises a plurality of single batteries to be detected.
The single battery to be detected refers to a single battery to be subjected to fault detection.
The equivalent circuit model may be a circuit model constructed based on real-time voltage data and real-time current data of the unit cell to be detected, and may be used to describe dynamic characteristics of the unit cell to be detected. For example, a davidian (Thevenin) equivalent circuit model is available.
The battery pack to be detected is a battery pack consisting of a plurality of single batteries to be detected.
In the embodiment of the disclosure, when the equivalent circuit model of the to-be-detected single battery is obtained, reliable data support can be provided for subsequent fault detection of the to-be-detected single battery.
S102: model parameters in the equivalent circuit model are identified, wherein the model parameters include an ohmic internal resistance value, a polarization capacitance value, and a polarization resistance value.
The model parameters refer to parameters constituting the equivalent circuit model.
The ohmic internal resistance consists of electrode material, electrolyte, diaphragm resistance and contact resistance of parts.
The polarized internal resistance refers to the resistance caused by polarization of the medium inside the battery or the capacitor. It can be represented as a resistive element, commonly referred to by the symbol R, connected in series between the positive and negative poles of a battery or capacitor p And (3) representing.
The polarized capacitance refers to the capacitance caused by polarization of the medium inside the battery or the capacitor. It can be modeled as a capacitive element, generally designated by the symbol C, connected in parallel between the positive and negative electrodes of a battery or capacitor p And (3) representing.
For example, in the embodiments of the present disclosure, a combined estimation method of Recursive Least-Squares (RLS) and extended kalman filter (EXTEND KALMAN FILTER, EKF) may be used to simultaneously estimate the SOC and model parameters of the unit cell to be detected
S103: calculating target anomaly factors corresponding to the model parameters, wherein the target anomaly factors comprise: a first anomaly factor corresponding to the ohmic internal resistance value, a second anomaly factor corresponding to the polarization capacitance value, and a third anomaly factor corresponding to the polarization resistance value.
The target anomaly factor is an anomaly factor corresponding to a model parameter calculated based on a local anomaly factor (Local Outlier Factor, LOF) method. The larger the value of the target abnormal factor is, the larger the probability of the single battery to be detected fault to which the corresponding model parameter belongs is.
The first abnormality factor, the second abnormality factor and the third abnormality factor are obtained by calculating an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value respectively.
In the embodiment of the disclosure, when the target anomaly factor corresponding to the model parameter is calculated, the obtained first anomaly factor, second anomaly factor and third anomaly factor can respectively describe anomaly degrees corresponding to the ohmic internal resistance value, the polarization capacitance value and the polarization resistance value, so that reliable reference information is provided for subsequent fault detection.
S104: calculating a target fault index corresponding to the target abnormality factor, wherein the target fault index comprises: a first fault indicator corresponding to the first anomaly factor, a second fault indicator corresponding to the second anomaly factor, and a third fault indicator corresponding to the third anomaly factor.
The target fault index is an index obtained by calculating a target abnormal factor by a pointer and can be used for describing fault information corresponding to the target abnormal factor.
The first fault index, the second fault index and the third fault index refer to fault indexes obtained by calculating the first abnormality factor, the second abnormality factor and the third abnormality factor respectively.
That is, in the embodiment of the present disclosure, after calculating the target abnormality factor corresponding to the model parameter, a target failure index corresponding to the target abnormality factor may be calculated, wherein the target failure index includes: the first fault index corresponding to the first abnormal factor, the second fault index corresponding to the second abnormal factor and the third fault index corresponding to the third abnormal factor provide reliable judgment basis for the follow-up determination of the fault detection result.
S105: and determining a fault detection result corresponding to the single battery to be detected based on the first fault index, the second fault index and the third fault index.
The fault detection result may be used to indicate whether the corresponding unit cell to be detected has failed.
In this embodiment, by acquiring an equivalent circuit model of a to-be-detected single battery, where the to-be-detected single battery belongs to a to-be-detected battery pack, the to-be-detected battery pack includes a plurality of to-be-detected single batteries, identifying model parameters in the equivalent circuit model, where the model parameters include an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value, and calculating a target anomaly factor corresponding to the model parameters, where the target anomaly factor includes: the method comprises the steps of calculating a target fault index corresponding to a target abnormal factor by a first abnormal factor corresponding to an ohmic internal resistance value, a second abnormal factor corresponding to a polarization capacitance value and a third abnormal factor corresponding to a polarization resistance value, wherein the target fault index comprises: the method comprises the steps of determining a fault detection result corresponding to a single battery to be detected based on a first fault index corresponding to a first abnormal factor, a second fault index corresponding to a second abnormal factor and a third fault index corresponding to a third abnormal factor, thereby effectively improving the accuracy and applicability of battery fault detection, improving the reliability and safety of an energy storage battery system, and reducing maintenance cost and time.
Fig. 2 is a flowchart illustrating a battery fault detection method according to another embodiment of the present disclosure.
As shown in fig. 2, the battery fault detection method includes:
s201: and obtaining an equivalent circuit model of the single battery to be detected, wherein the single battery to be detected belongs to a battery pack to be detected, and the battery pack to be detected comprises a plurality of single batteries to be detected.
S202: model parameters in the equivalent circuit model are identified, wherein the model parameters include an ohmic internal resistance value, a polarization capacitance value, and a polarization resistance value.
The descriptions of S201 and S202 may be specifically referred to the above embodiments, and are not repeated herein.
S203: and determining the number of the batteries of the single batteries to be detected contained in the battery pack to be detected.
The number of the batteries refers to the number of the single batteries to be detected contained in the battery pack to be detected.
It can be understood that the number of batteries determines the number of corresponding model parameters of the battery pack, and the number of model parameters may affect the calculation process of the anomaly factors, so that when determining the number of batteries of the unit batteries to be detected included in the battery pack to be detected, reliable data support can be provided for subsequent calculation of the anomaly factors.
S204: and determining a first abnormality factor based on the number of the batteries and ohmic internal resistance values corresponding to the plurality of single batteries to be detected.
S205: and determining a second abnormality factor based on the number of the batteries and polarization capacitance values corresponding to the plurality of single batteries to be detected.
S206: and determining a third abnormal factor based on the number of the batteries and polarization resistance values corresponding to the plurality of single batteries to be detected.
That is, in the embodiment of the present disclosure, after identifying the model parameters in the equivalent circuit model, the number of the batteries of the to-be-detected unit cells included in the to-be-detected battery pack may be determined, the first abnormal factor may be determined based on the number of the batteries and the ohmic internal resistance values corresponding to the plurality of to-be-detected unit cells, the second abnormal factor may be determined based on the number of the batteries and the polarization capacitance values corresponding to the plurality of to-be-detected unit cells, and the third abnormal factor may be determined based on the number of the batteries and the polarization resistance values corresponding to the plurality of to-be-detected unit cells, thereby, the suitability between the abnormal factor calculation process and the personalized application scenario may be effectively improved, and the accuracy of the obtained abnormal factor may be effectively improved.
S207: a first average value and a first sample standard deviation of the plurality of first abnormality factors are determined, and a first fault index is calculated based on the first abnormality factors, the first average value and the first sample standard deviation.
The first average value refers to an average value of a plurality of first anomaly factors. The first sample standard deviation refers to the sample standard deviations of the plurality of first anomaly factors.
S208: and determining a second average value and a second sample standard deviation of the plurality of second abnormal factors, and calculating a second fault index based on the second abnormal factors, the second average value and the second sample standard deviation.
The second average value refers to an average value of a plurality of second abnormality factors. The second sample standard deviation refers to the sample standard deviations of the plurality of second anomaly factors.
S209: and determining a third average value and a third sample standard deviation of the plurality of third abnormal factors, and calculating a third fault index based on the third abnormal factors, the third average value and the third sample standard deviation.
The third average value refers to an average value of a plurality of third abnormal factors. The third sample standard deviation refers to the sample standard deviations of the plurality of third anomaly factors.
For example, the present disclosure implementsIn an example, a first abnormality factor corresponding to each battery to be detected in the battery pack to be detected may be obtained to form a first abnormality factor set, and then the first abnormality factors are obtainedCorresponding first failure index- >It can be defined as:
wherein,,and S represents the average value and the sample standard deviation of a plurality of first anomaly factors in the first anomaly factor set respectively, and is defined as follows:
it can be appreciated that the process of acquiring the second fault indicator and the third fault indicator may refer to the process of acquiring the first fault indicator, which is not described herein.
That is, in the embodiment of the present disclosure, after the first anomaly factor, the second anomaly factor, and the third anomaly factor are obtained, a first average value and a first sample standard deviation of the plurality of first anomaly factors may be determined, and based on the first anomaly factor, the first average value, and the first sample standard deviation, a first fault indicator may be calculated, a second average value and a second sample standard deviation of the plurality of second anomaly factors may be determined, and based on the second anomaly factor, the second average value, and the second sample standard deviation, a second fault indicator may be calculated, and a third average value and a third sample standard deviation of the plurality of third anomaly factors may be determined, and based on the third anomaly factor, the third average value, and the third sample standard deviation, a third fault indicator may be calculated, thereby effectively improving a description effect of the obtained fault indicator.
S210: and determining a fault detection result corresponding to the single battery to be detected based on the first fault index, the second fault index and the third fault index.
The description of S210 may be specifically referred to the above embodiments, and will not be repeated here.
In this embodiment, the number of the cells to be detected included in the battery pack to be detected is determined, the first abnormal factor is determined based on the number of the cells and the ohmic internal resistance values corresponding to the plurality of the cells to be detected, the second abnormal factor is determined based on the number of the cells and the polarization capacitance values corresponding to the plurality of the cells to be detected, and the third abnormal factor is determined based on the number of the cells and the polarization resistance values corresponding to the plurality of the cells to be detected, so that the suitability between the abnormal factor calculation process and the personalized application scene can be effectively improved, and the accuracy of the obtained abnormal factor can be effectively improved. The first fault index is calculated by determining a first average value and a first sample standard deviation of a plurality of first abnormal factors and based on the first abnormal factors, the first average value and the first sample standard deviation, the second average value and the second sample standard deviation of a plurality of second abnormal factors are determined, the second fault index is calculated and obtained based on the second abnormal factors, the second average value and the second sample standard deviation, the third average value and the third sample standard deviation of a plurality of third abnormal factors are determined, and the third fault index is calculated and obtained based on the third abnormal factors, the third average value and the third sample standard deviation, so that the description effect of the obtained fault index can be effectively improved.
Fig. 3 is a flowchart illustrating a battery fault detection method according to another embodiment of the present disclosure.
As shown in fig. 3, the battery fault detection method includes:
s301: and obtaining an equivalent circuit model of the single battery to be detected, wherein the single battery to be detected belongs to a battery pack to be detected, and the battery pack to be detected comprises a plurality of single batteries to be detected.
S302: model parameters in the equivalent circuit model are identified, wherein the model parameters include an ohmic internal resistance value, a polarization capacitance value, and a polarization resistance value.
S303: calculating target anomaly factors corresponding to the model parameters, wherein the target anomaly factors comprise: a first anomaly factor corresponding to the ohmic internal resistance value, a second anomaly factor corresponding to the polarization capacitance value, and a third anomaly factor corresponding to the polarization resistance value.
S304: calculating a target fault index corresponding to the target abnormality factor, wherein the target fault index comprises: a first fault indicator corresponding to the first anomaly factor, a second fault indicator corresponding to the second anomaly factor, and a third fault indicator corresponding to the third anomaly factor.
The descriptions of S301 to S304 may be specifically referred to the above embodiments, and are not repeated herein.
S305: a fault threshold is determined.
The fault threshold value can be used for comparing with a target fault index so as to judge whether the battery of the battery fails or not.
Optionally, in some embodiments, when determining the fault critical value, the fault detection requirement information of the to-be-detected single battery may be obtained, the target confidence value is determined according to the fault detection requirement information, and the fault critical value is obtained from a preset critical value table based on the number of the batteries and the target confidence value, where the preset critical value table includes a plurality of reference critical values, and the fault critical value belongs to a plurality of reference critical values, so that the corresponding fault critical value can be flexibly configured according to a personalized application scenario, thereby effectively improving the applicability of the obtained fault critical value and effectively improving the fault detection effect.
S306: a first comparison result of the first fault indicator and the fault threshold is determined.
The first comparison result refers to a comparison result between the first fault index and the fault critical value, and may be used to indicate a magnitude relation between the first fault index and the fault critical value.
S307: and determining a second comparison result of the second fault index and the fault critical value.
The second comparison result refers to a comparison result between the second fault index and the fault critical value, and may be used to indicate a magnitude relation between the second fault index and the fault critical value.
S308: and determining a third comparison result of the third fault index and the fault critical value.
The third comparison result refers to a comparison result between the third fault index and the fault critical value, and may be used to indicate a magnitude relation between the third fault index and the fault critical value.
S309: and determining a fault detection result according to the first comparison result, the second comparison result and the third comparison result.
Optionally, in some embodiments, when determining the fault detection result according to the first comparison result, the second comparison result, and the third comparison result, it may be determined that the to-be-detected single battery fails in response to the first comparison result, the second comparison result, and the third comparison result meeting the preset conditions, and it may be determined that the to-be-detected single battery fails in response to the first comparison result, the second comparison result, and the third comparison result not meeting the preset conditions, thereby providing a reliable judgment basis for determining that the to-be-detected single battery fails.
The preset conditions refer to judgment conditions configured for the first comparison result, the second comparison result and the third comparison result in advance.
Optionally, in some embodiments, the preset conditions include: the first comparison result is that the first fault index is smaller than the fault critical value; the second comparison result is that the second fault index is smaller than the fault critical value; and the third comparison result is that the third fault index is smaller than the fault critical value. That is, in the embodiment of the present disclosure, it may be determined that the to-be-detected single battery fails only when the first failure index, the second failure index, and the third failure index corresponding to the to-be-detected single battery are all smaller than the failure threshold value. When any one of the first fault index, the second fault index and the third fault index is greater than or equal to a fault critical value, it can be determined that the single battery to be detected has failed.
That is, in the embodiment of the disclosure, after calculating the target fault index corresponding to the target abnormality factor, the fault critical value may be determined, the first comparison result of the first fault index and the fault critical value may be determined, the second comparison result of the second fault index and the fault critical value may be determined, the third comparison result of the third fault index and the fault critical value may be determined, and the fault detection result may be determined according to the first comparison result, the second comparison result and the third comparison result, thereby, the multi-fault point detection of the single battery to be detected may be realized, and the reliability of the battery fault detection may be effectively improved.
In this embodiment, by determining the fault threshold, determining the first comparison result of the first fault indicator and the fault threshold, determining the second comparison result of the second fault indicator and the fault threshold, determining the third comparison result of the third fault indicator and the fault threshold, and determining the fault detection result according to the first comparison result, the second comparison result and the third comparison result, the multi-fault point detection of the single battery to be detected can be realized, and the reliability of the battery fault detection can be effectively improved. The method comprises the steps of obtaining fault detection requirement information of a single battery to be detected, determining a target confidence value according to the fault detection requirement information, and obtaining a fault critical value from a preset critical value table based on the number of the batteries and the target confidence value, wherein the preset critical value table comprises a plurality of reference critical values, and the fault critical value belongs to the plurality of reference critical values. The first comparison result, the second comparison result and the third comparison result meet the preset conditions, the fact that the single battery to be detected fails is determined, and the fact that the single battery to be detected fails is determined in response to the fact that the first comparison result, the second comparison result and the third comparison result do not meet the preset conditions, so that reliable judgment basis can be provided for determining that the single battery to be detected fails.
It will be appreciated that the number of components,the local anomaly factor may also be referred to as a local outlier factor, for example, as shown in FIG. 4, FIG. 4 is a flow chart of a fault detection method using the local outlier factor according to the present disclosure, wherein U k And I K Is the real-time voltage value and the real-time current value of the single battery to be detected, U ocv Refers to open circuit voltage, R in Refers to ohmic internal resistance, R p Refers to polarization resistance, C p Refers to polarization capacitance, U t Refers to the terminal voltage, y k Refers to an estimated voltage obtained based on a battery model, x= { R in,i ,R p,i ,C p,i The expression "p" refers to a sample data set composed of ohmic internal resistances, polarization resistances and polarization capacitances based on a plurality of unit cells to be detected, the expression "p" refers to one sample data in the sample data set X,is a set of m nearest neighbors of p, < ->Represents the local reachable density of p, +.>Represents the distance of reachability of p relative to o, < >>Is the Euclidean distance between p and o. />An m distance, p, defined as the distance between o and its mth nearest neighbor,is an outlier factor set, < ->And->Respectively represent R in 、R p And C p Is a fault indicator of (1). Glabros (Grubbs) criteria, belonging to a normally distributed distributionBranch, which is the absolute value of the residual error of a measured value |Vi| of>Gg, judging that the value has larger error and rejecting. An outlier filter based on Grubbs criteria is designed in fig. 4 to check if the outlier factor is within its normal range.
Fig. 5 is a schematic structural diagram of a battery fault detection device according to an embodiment of the present disclosure.
As shown in fig. 5, the battery failure detection device 50 includes:
the obtaining module 501 is configured to obtain an equivalent circuit model of a to-be-detected single battery, where the to-be-detected single battery belongs to a to-be-detected battery pack, and the to-be-detected battery pack includes a plurality of to-be-detected single batteries;
the identifying module 502 is configured to identify model parameters in the equivalent circuit model, where the model parameters include an ohmic internal resistance value, a polarization capacitance value, and a polarization resistance value;
a first calculation module 503, configured to calculate a target anomaly factor corresponding to the model parameter, where the target anomaly factor includes: a first anomaly factor corresponding to the ohmic internal resistance value, a second anomaly factor corresponding to the polarization capacitance value, and a third anomaly factor corresponding to the polarization resistance value;
a second calculating module 504, configured to calculate a target fault indicator corresponding to the target abnormality factor, where the target fault indicator includes: a first fault indicator corresponding to the first anomaly factor, a second fault indicator corresponding to the second anomaly factor, and a third fault indicator corresponding to the third anomaly factor;
The determining module 505 is configured to determine a fault detection result corresponding to the to-be-detected unit cell based on the first fault indicator, the second fault indicator, and the third fault indicator.
It should be noted that the foregoing explanation of the battery fault detection method is also applicable to the battery fault detection device of the present embodiment, and is not repeated here.
In this embodiment, by acquiring an equivalent circuit model of a to-be-detected single battery, where the to-be-detected single battery belongs to a to-be-detected battery pack, the to-be-detected battery pack includes a plurality of to-be-detected single batteries, identifying model parameters in the equivalent circuit model, where the model parameters include an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value, and calculating a target anomaly factor corresponding to the model parameters, where the target anomaly factor includes: the method comprises the steps of calculating a target fault index corresponding to a target abnormal factor by a first abnormal factor corresponding to an ohmic internal resistance value, a second abnormal factor corresponding to a polarization capacitance value and a third abnormal factor corresponding to a polarization resistance value, wherein the target fault index comprises: the method comprises the steps of determining a fault detection result corresponding to a single battery to be detected based on a first fault index corresponding to a first abnormal factor, a second fault index corresponding to a second abnormal factor and a third fault index corresponding to a third abnormal factor, thereby effectively improving the accuracy and applicability of battery fault detection, improving the reliability and safety of an energy storage battery system, and reducing maintenance cost and time.
Fig. 6 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure. The electronic device 12 shown in fig. 6 is merely an example and should not be construed as limiting the functionality and scope of use of the disclosed embodiments.
As shown in fig. 6, the electronic device 12 is in the form of a general purpose computing device. Components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry Standard architecture (Industry Standard Architecture; hereinafter ISA) bus, micro channel architecture (Micro Channel Architecture; hereinafter MAC) bus, enhanced ISA bus, video electronics standards Association (Video Electronics Standards Association; hereinafter VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnection; hereinafter PCI) bus.
Electronic device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory; hereinafter: RAM) 30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard disk drive").
Although not shown in fig. 6, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a compact disk read only memory (Compact Disc Read Only Memory; hereinafter CD-ROM), digital versatile read only optical disk (Digital Video Disc Read Only Memory; hereinafter DVD-ROM), or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the various embodiments of the disclosure.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods in the embodiments described in this disclosure.
The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a person to interact with the electronic device 12, and/or any devices (e.g., network card, modem, etc.) that enable the electronic device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks, such as a local area network (Local Area Network; hereinafter: LAN), a wide area network (Wide Area Network; hereinafter: WAN) and/or a public network, such as the Internet, via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 over the bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the battery failure detection method mentioned in the foregoing embodiment.
In order to implement the above-described embodiments, the present disclosure also proposes a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a battery failure detection method as proposed in the foregoing embodiments of the present disclosure.
To achieve the above-described embodiments, the present disclosure also proposes a computer program product which, when executed by an instruction processor in the computer program product, performs a battery failure detection method as proposed in the foregoing embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
It should be noted that in the description of the present disclosure, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present disclosure, unless otherwise indicated, the meaning of "a plurality" is two or more.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
Furthermore, each functional unit in the embodiments of the present disclosure may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the present disclosure, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the present disclosure.

Claims (7)

1. A battery failure detection method, characterized by comprising:
obtaining an equivalent circuit model of a single battery to be detected, wherein the single battery to be detected belongs to a battery pack to be detected, and the battery pack to be detected comprises a plurality of single batteries to be detected;
identifying model parameters in the equivalent circuit model, wherein the model parameters comprise an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value;
calculating a target abnormality factor corresponding to the model parameter, wherein the target abnormality factor comprises: the method comprises the steps of calculating a first abnormal factor corresponding to the ohmic internal resistance value, a second abnormal factor corresponding to the polarization capacitance value and a third abnormal factor corresponding to the polarization resistance value, wherein a target abnormal factor is an abnormal factor corresponding to a model parameter calculated based on a local abnormal factor method, and the larger the value of the target abnormal factor is, the larger the probability that the corresponding model parameter belongs to a single battery to be detected is;
Calculating a target fault index corresponding to the target abnormal factor, wherein the target fault index comprises: a first fault indicator corresponding to the first anomaly factor, a second fault indicator corresponding to the second anomaly factor, and a third fault indicator corresponding to the third anomaly factor;
determining a fault detection result corresponding to the single battery to be detected based on the first fault index, the second fault index and the third fault index;
the calculating the target fault index corresponding to the target abnormal factor comprises the following steps:
determining a first average value and a first sample standard deviation of a plurality of first abnormality factors, and calculating to obtain the first fault index based on the first abnormality factors, the first average value and the first sample standard deviation;
determining a second average value and a second sample standard deviation of a plurality of second abnormality factors, and calculating to obtain the second fault index based on the second abnormality factors, the second average value and the second sample standard deviation;
determining a third average value and a third sample standard deviation of the plurality of third abnormal factors, calculating to obtain the third fault index based on the third abnormal factors, the third average value and the third sample standard deviation, and determining a fault detection result corresponding to the to-be-detected single battery based on the first fault index, the second fault index and the third fault index, wherein the method comprises the following steps:
Determining a fault threshold, the determining the fault threshold comprising:
acquiring fault detection requirement information of the single battery to be detected;
determining a target confidence value according to the fault detection requirement information;
acquiring the fault critical value from a preset critical value table based on the number of batteries and the target confidence value, wherein the preset critical value table comprises a plurality of reference critical values, and the fault critical value belongs to the plurality of reference critical values;
determining a first comparison result of the first fault index and the fault threshold;
determining a second comparison result of the second fault index and the fault critical value;
determining a third comparison result of the third fault index and the fault critical value;
and determining the fault detection result according to the first comparison result, the second comparison result and the third comparison result.
2. The method of claim 1, wherein the calculating a target anomaly factor corresponding to the model parameter comprises:
determining the number of the to-be-detected single batteries contained in the to-be-detected battery pack;
determining the first abnormality factor based on the number of batteries and the ohmic internal resistance values corresponding to the plurality of to-be-detected single batteries;
Determining the second abnormality factor based on the number of batteries and the polarization capacitance values corresponding to the plurality of to-be-detected single batteries;
and determining the third abnormality factor based on the number of the batteries and the polarization resistance values corresponding to the plurality of the to-be-detected single batteries.
3. The method of claim 1, wherein the determining the fault detection result based on the first comparison result, the second comparison result, and the third comparison result comprises:
responding to the first comparison result, the second comparison result and the third comparison result to meet a preset condition, and determining that the single battery to be detected has no fault;
and responding to the first comparison result, the second comparison result and the third comparison result not meeting the preset condition, and determining that the single battery to be detected fails.
4. A method according to claim 3, wherein the preset conditions include:
the first comparison result is that the first fault index is smaller than the fault critical value;
the second comparison result is that the second fault index is smaller than the fault critical value;
And the third comparison result is that the third fault index is smaller than the fault critical value.
5. A battery failure detection apparatus, characterized by comprising:
the device comprises an acquisition module, a detection module and a detection module, wherein the acquisition module is used for acquiring an equivalent circuit model of a single battery to be detected, the single battery to be detected belongs to a battery pack to be detected, and the battery pack to be detected comprises a plurality of single batteries to be detected;
the identification module is used for identifying model parameters in the equivalent circuit model, wherein the model parameters comprise an ohmic internal resistance value, a polarization capacitance value and a polarization resistance value;
the first calculation module is used for calculating a target abnormal factor corresponding to the model parameter, wherein the target abnormal factor comprises: the method comprises the steps of calculating a first abnormal factor corresponding to the ohmic internal resistance value, a second abnormal factor corresponding to the polarization capacitance value and a third abnormal factor corresponding to the polarization resistance value, wherein a target abnormal factor is an abnormal factor corresponding to a model parameter calculated based on a local abnormal factor method, and the larger the value of the target abnormal factor is, the larger the probability that the corresponding model parameter belongs to a single battery to be detected is;
The second calculation module is configured to calculate a target fault indicator corresponding to the target abnormality factor, where the target fault indicator includes: a first fault indicator corresponding to the first anomaly factor, a second fault indicator corresponding to the second anomaly factor, and a third fault indicator corresponding to the third anomaly factor;
the determining module is used for determining a fault detection result corresponding to the single battery to be detected based on the first fault index, the second fault index and the third fault index;
the first calculation module is further configured to calculate a target fault indicator corresponding to the target abnormality factor, and includes:
determining a first average value and a first sample standard deviation of a plurality of first abnormality factors, and calculating to obtain the first fault index based on the first abnormality factors, the first average value and the first sample standard deviation;
determining a second average value and a second sample standard deviation of a plurality of second abnormality factors, and calculating to obtain the second fault index based on the second abnormality factors, the second average value and the second sample standard deviation;
determining a third average value and a third sample standard deviation of a plurality of third abnormal factors, and calculating to obtain a third fault index based on the third abnormal factors, the third average value and the third sample standard deviation;
The determining module is further configured to determine a fault threshold, where determining the fault threshold includes:
acquiring fault detection requirement information of the single battery to be detected;
determining a target confidence value according to the fault detection requirement information;
acquiring the fault critical value from a preset critical value table based on the number of batteries and the target confidence value, wherein the preset critical value table comprises a plurality of reference critical values, and the fault critical value belongs to the plurality of reference critical values;
determining a first comparison result of the first fault index and the fault threshold;
determining a second comparison result of the second fault index and the fault critical value;
determining a third comparison result of the third fault index and the fault critical value;
and determining the fault detection result according to the first comparison result, the second comparison result and the third comparison result.
6. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
7. A non-transitory computer readable storage medium storing computer instructions, wherein the computer instructions are for causing the computer to perform the method of any one of claims 1-4.
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