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

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 device and storage medium

technical field

The disclosure relates to the technical field of intelligent fault diagnosis of battery energy storage systems, and in particular to a battery fault detection method, device, electronic equipment and storage medium.

Background technique

With the rapid development of new energy technologies, energy storage battery systems have become an important part of renewable energy and smart grids. The performance and life of the energy storage battery directly affect the efficiency and stability of the energy storage system. In the energy storage battery system, the battery voltage is one of the important parameters, which can reflect the state and performance of the battery. However, because the energy storage battery is affected by many factors, such as temperature, charge and discharge cycle, service time, etc., the battery voltage often has certain fluctuations and instability. In actual use, when the voltage of the energy storage battery changes abnormally, it may cause failures and accidents of the energy storage system. Therefore, there is a need for an energy storage battery voltage fault detection method based on abnormal point detection.

In related technologies, when detecting battery faults based on abnormal points, usually only a single abnormal value can be detected, and it is difficult to detect multiple faults.

In this way, the applicability of the detection process and the accuracy of the detection results cannot be guaranteed.

Contents of the invention

The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

Therefore, the purpose of this disclosure is to propose a battery fault detection method, device, electronic equipment, and storage medium, which can effectively improve the accuracy and applicability of battery fault detection, and can improve the reliability and safety of the energy storage battery system. While reducing maintenance costs and time.

The battery fault detection method proposed in the embodiment of the first aspect of the present disclosure includes:

Obtaining an equivalent circuit model of a single battery to be tested, wherein the single battery to be tested belongs to a battery pack to be tested, and the battery pack to be tested includes a plurality of single batteries to be tested;

Identifying model parameters in the equivalent circuit model, wherein the model parameters include ohmic internal resistance, polarization capacitance and polarization resistance;

calculating a target anomaly factor corresponding to the model parameter, wherein the target anomaly factor includes: a first anomalous factor corresponding to the ohmic internal resistance value, a second anomalous factor corresponding to the polarization capacitance value, and a third outlier factor corresponding to the polarization resistance value;

calculating a target fault index corresponding to the target abnormal factor, wherein the target fault index includes: 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;

Based on the first fault index, the second fault index and the third fault index, a fault detection result corresponding to the single battery to be detected is determined.

The battery fault detection method proposed in the embodiment of the first aspect of the present disclosure obtains the equivalent circuit model of the single battery to be tested, wherein the single battery to be tested belongs to the battery pack to be tested, and the battery pack to be tested contains multiple For a single battery, identify the model parameters in the equivalent circuit model, where the model parameters include ohmic internal resistance, polarization capacitance, and polarization resistance, and calculate the target anomaly factors corresponding to the model parameters, where the target anomaly factors include : The first abnormal factor corresponding to the ohmic internal resistance value, the second abnormal factor corresponding to the polarization capacitance value, and the third abnormal factor corresponding to the polarization resistance value, calculate the target fault index corresponding to the target abnormal factor, where , the target fault 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, based on the first fault index, the second The second fault index and the third fault index determine the fault detection result corresponding to the single battery to be detected, thus, the accuracy and applicability of battery fault detection can be effectively improved, and the reliability and safety of the energy storage battery system can be improved , while reducing maintenance costs and time.

The battery fault detection device proposed in the embodiment of the second aspect of the present disclosure includes:

An acquisition module, configured to acquire an equivalent circuit model of a single battery to be tested, wherein the single battery to be tested belongs to a battery pack to be tested, and the battery pack to be tested includes a plurality of single batteries to be tested;

An identification module, configured to identify model parameters in the equivalent circuit model, wherein the model parameters include ohmic internal resistance, polarization capacitance and polarization resistance;

The first calculation module is used to calculate the target abnormal factor corresponding to the model parameter, wherein the target abnormal factor includes: a first abnormal factor corresponding to the ohmic internal resistance value, a first abnormal factor corresponding to the polarization capacitance value A second anomalous factor of , and a third anomalous factor corresponding to the polarization resistance value;

The second calculation module is used to calculate the target fault index corresponding to the target abnormal factor, wherein the target fault index includes: the first fault index corresponding to the first abnormal factor, and the second abnormal factor a corresponding second fault index, and a third fault index corresponding to the third abnormal factor;

A determining module, configured to determine 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 battery fault detection device proposed in the embodiment of the second aspect of the present disclosure obtains the equivalent circuit model of the single battery to be tested, wherein the single battery to be tested belongs to the battery pack to be tested, and the battery pack to be tested contains multiple For a single battery, identify the model parameters in the equivalent circuit model, where the model parameters include ohmic internal resistance, polarization capacitance, and polarization resistance, and calculate the target anomaly factors corresponding to the model parameters, where the target anomaly factors include : The first abnormal factor corresponding to the ohmic internal resistance value, the second abnormal factor corresponding to the polarization capacitance value, and the third abnormal factor corresponding to the polarization resistance value, calculate the target fault index corresponding to the target abnormal factor, where , the target fault 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, based on the first fault index, the second The second fault index and the third fault index determine the fault detection result corresponding to the single battery to be detected, thus, the accuracy and applicability of battery fault detection can be effectively improved, and the reliability and safety of the energy storage battery system can be improved , while reducing maintenance costs and time.

The electronic device proposed in the embodiment of the third aspect of the present disclosure includes: a memory, a processor, and a computer program stored in the memory and operable on the processor, and the processor implements the first aspect of the present disclosure when executing the program. The battery failure detection method proposed in the embodiment.

The embodiment of the fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the battery failure detection method as proposed in the embodiment of the first aspect of the present disclosure is implemented.

The embodiment of the fifth aspect of the present disclosure provides a computer program product. When the instructions in the computer program product are executed by a processor, the method for detecting a battery failure as proposed in the embodiment of the first aspect of the present disclosure is executed.

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.

Description of drawings

The above and/or additional aspects and advantages of the present disclosure will become apparent and understandable from the following description of the embodiments in conjunction with the accompanying drawings, wherein:

FIG. 1 is a schematic flowchart of a battery fault detection method proposed by an embodiment of the present disclosure;

2 is a schematic flowchart of a battery fault detection method proposed by another embodiment of the present disclosure;

3 is a schematic flowchart of a battery fault detection method proposed by another embodiment of the present disclosure;

4 is a flow chart of a fault detection method using local outlier factors proposed according to the present disclosure;

5 is a schematic structural diagram of a battery fault detection device proposed by an embodiment of the present disclosure;

FIG. 6 shows a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.

Detailed ways

Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present disclosure and should not be construed as limiting the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications and equivalents coming within the spirit and scope of the appended claims.

FIG. 1 is a schematic flowchart of a battery fault detection method proposed by an embodiment of the present disclosure.

Wherein, it should be noted that the execution body of the battery fault detection method of this embodiment is a battery fault detection device, which can be implemented by software and/or hardware, and which can be configured in an electronic device, and the electronic device can include But not limited to the terminal, server, etc., for example, the terminal can be a mobile phone, a handheld computer, and the like.

As shown in Figure 1, the battery fault detection method includes:

S101: Obtain an equivalent circuit model of a single battery to be tested, wherein the single battery to be tested belongs to a battery pack to be tested, and the battery pack to be tested includes a plurality of single batteries to be tested.

Wherein, the single battery to be detected refers to a single battery to be detected for a fault.

Wherein, the equivalent circuit model may refer to a circuit model constructed based on real-time voltage data and real-time current data of the single battery to be tested, which can be used to describe the dynamic characteristics of the single battery to be tested. For example, it may be a Thevenin equivalent circuit model.

Wherein, the battery pack to be tested refers to a battery pack composed of a plurality of single cells to be tested.

In the embodiment of the present disclosure, when the equivalent circuit model of the single battery to be tested is obtained, reliable data support can be provided for subsequent fault detection of the single battery to be tested.

S102: Identify model parameters in the equivalent circuit model, where the model parameters include ohmic internal resistance, polarization capacitance, and polarization resistance.

Wherein, the model parameters refer to the parameters constituting the above-mentioned equivalent circuit model.

Among them, the ohmic internal resistance is composed of electrode material, electrolyte, diaphragm resistance and contact resistance of various parts.

Among them, the polarization internal resistance refers to the resistance caused by the polarization of the internal medium of the battery or capacitor. It can be expressed as a resistive element connected in series between the positive and negative poles of a battery or capacitor, usually represented by the symbol _Rp .

Among them, the polarization capacitance refers to the capacitance caused by the polarization of the internal medium of the battery or capacitor. It can be modeled as a capacitive element connected in parallel between the positive and negative terminals of a battery or capacitor, usually denoted by the symbol _Cp .

For example, in the embodiment of the present disclosure, a joint estimation method of Recursive Least-Squares (RLS) and Extended Kalman Filter (EXTEND KALMAN FILTER, EKF) can be used to simultaneously estimate the SOC and Model parameters

S103: Calculate the target abnormal factor corresponding to the model parameters, wherein the target abnormal factor includes: a first abnormal factor corresponding to the ohmic internal resistance value, a second abnormal factor corresponding to the polarization capacitance value, and a second abnormal factor corresponding to the polarization resistance value the third outlier factor.

Among them, the target outlier factor refers to the outlier factor corresponding to the model parameters calculated based on the local outlier factor (Local Outlier Factor, LOF) method. The larger the value of the target anomaly factor, the greater the probability of the failure of the single battery to be detected to which the corresponding model parameter belongs.

Wherein, the first abnormal factor, the second abnormal factor and the third abnormal factor are abnormal factors calculated for the ohmic internal resistance value, the polarization capacitance value and the polarization resistance value respectively.

In the embodiment of the present disclosure, when calculating the target anomaly factor corresponding to the model parameters, the first anomaly factor, the second anomaly factor, and the third anomaly factor obtained can describe the ohmic internal resistance, polarization capacitance, and polarization resistance respectively The degree of abnormality corresponding to the value can provide reliable reference information for subsequent fault detection.

S104: Calculate the target fault index corresponding to the target abnormal factor, wherein the target fault 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 abnormal factor Corresponding third failure indicator.

Wherein, the target fault index refers to an index calculated for the target abnormal factor, which can be used to describe the fault information corresponding to the target abnormal factor.

Wherein, the first fault index, the second fault index and the third fault index refer to the fault indexes calculated for the first abnormality factor, the second abnormality factor and the third abnormality factor respectively.

That is to say, in the embodiment of the present disclosure, after calculating the target anomaly factor corresponding to the model parameters, the target failure index corresponding to the target anomaly factor can be calculated, wherein the target failure index includes: the first abnormal factor corresponding to the first The fault index, the second fault index corresponding to the second abnormal factor, and the third fault index corresponding to the third abnormal factor provide a reliable judgment basis for subsequent determination of fault detection results.

S105: Based on the first fault index, the second fault index and the third fault index, determine a fault detection result corresponding to the single battery to be detected.

Wherein, the fault detection result may be used to indicate whether a fault occurs to the single battery to be detected.

In this embodiment, by obtaining the equivalent circuit model of the single battery to be tested, wherein the single battery to be tested belongs to the battery pack to be tested, and the battery pack to be tested contains a plurality of single cells to be tested, and the equivalent circuit model is identified The model parameters in , where the model parameters include ohmic internal resistance, polarization capacitance and polarization resistance, calculate the target anomaly factor corresponding to the model parameters, wherein the target anomaly factor includes: the first ohmic internal resistance corresponding to An abnormal factor, a second abnormal factor corresponding to the polarization capacitance value, and a third abnormal factor corresponding to the polarization resistance value, calculate a target fault index corresponding to the target abnormal factor, wherein the target fault index includes: The first fault index corresponding to the abnormal factor, the second fault index corresponding to the second abnormal factor, and the third fault index corresponding to the third abnormal factor, based on the first fault index, the second fault index and the third fault index, By determining the fault detection result corresponding to the single battery to be detected, the accuracy and applicability of battery fault detection can be effectively improved, the reliability and safety of the energy storage battery system can be improved, and maintenance costs and time can be reduced.

FIG. 2 is a schematic flowchart of a battery fault detection method proposed by another embodiment of the present disclosure.

As shown in Figure 2, the battery fault detection method includes:

S201: Obtain an equivalent circuit model of a single battery to be tested, wherein the single battery to be tested belongs to a battery pack to be tested, and the battery pack to be tested includes a plurality of single batteries to be tested.

S202: Identify model parameters in the equivalent circuit model, where the model parameters include ohmic internal resistance, polarization capacitance, and polarization resistance.

For descriptions of S201 and S202, reference may be made to the foregoing embodiments, and details are not repeated here.

S203: Determine the battery quantity of the single batteries to be tested included in the battery pack to be tested.

Wherein, the number of batteries refers to the number of single batteries to be tested contained in the battery pack to be tested.

It can be understood that the number of batteries determines the number of model parameters corresponding to the same type of battery pack, and the number of model parameters may affect the calculation process of abnormal factors. When the number of batteries is large, it can provide reliable data support for the subsequent calculation of abnormal factors.

S204: Determine a first abnormality factor based on the number of batteries and the ohmic internal resistance values corresponding to a plurality of single batteries to be detected.

S205: Determine a second abnormality factor based on the number of batteries and the polarization capacitance values corresponding to a plurality of single batteries to be detected.

S206: Determine a third abnormality factor based on the number of batteries and the polarization resistance values corresponding to a plurality of single batteries to be detected.

That is to say, in the embodiment of the present disclosure, after identifying the model parameters in the equivalent circuit model, the battery quantity of the single battery to be tested contained in the battery pack to be tested can be determined, based on the number of batteries, and the number of batteries to be tested The ohmic internal resistance value corresponding to the single battery, determine the first abnormal factor, and determine the second abnormal factor based on the number of batteries, and the polarization capacitance corresponding to a plurality of single batteries to be detected, based on the number of batteries, and the plurality of The polarization resistance value corresponding to the single battery to be detected determines the third abnormal factor, thereby effectively improving the adaptability between the abnormal factor calculation process and the personalized application scenario, and effectively improving the accuracy of the obtained abnormal factor.

S207: Determine a first average value and a first sample standard deviation of a plurality of first abnormal factors, and calculate a first fault index based on the first abnormal factor, the first average value, and the first sample standard deviation.

Wherein, the first average value refers to the average value of a plurality of first abnormal factors. The first sample standard deviation refers to the sample standard deviation of multiple first abnormal factors.

S208: Determine a second average value and a second sample standard deviation of a plurality of second abnormal factors, and calculate a second fault index based on the second abnormal factors, the second average value, and the second sample standard deviation.

Wherein, the second average value refers to the average value of a plurality of second abnormal factors. The second sample standard deviation refers to the sample standard deviation of multiple second abnormal factors.

S209: Determine a third average value and a third sample standard deviation of a plurality of third abnormal factors, and calculate a third fault index based on the third abnormal factor, the third average value, and the third sample standard deviation.

Wherein, the third average value refers to the average value of multiple third abnormal factors. The third sample standard deviation refers to the sample standard deviation of multiple third abnormal factors.

For example, in the embodiment of the present disclosure, the first abnormal factor corresponding to each battery to be detected in the battery pack to be detected can be obtained to form the first abnormal factor set, then the first abnormal factor Corresponding first failure indicator/> , which can be defined as:

in, and S denote the mean and sample standard deviation of multiple first anomalous factors in the first anomalous factor set respectively, which are defined as follows:

It can be understood that, for the obtaining process of the second fault index and the third fault index, reference may be made to the above-mentioned obtaining process of the first fault index, which will not be repeated here.

That is to say, in the embodiment of the present disclosure, after obtaining the first abnormal factor, the second abnormal factor, and the third abnormal factor, the first average value and the first sample standard deviation of multiple first abnormal factors can be determined, and based on The first abnormal factor, the first average value and the first sample standard deviation are calculated to obtain the first fault index, the second average value and the second sample standard deviation of multiple second abnormal factors are determined, and based on the second abnormal factor, The second average value and the second sample standard deviation are calculated to obtain the second fault index, and the third average value and the third sample standard deviation of a plurality of third abnormal factors are determined, and based on the third abnormal factor, the third average value and the first The three-sample standard deviation is used to calculate the third fault index, which can effectively improve the description effect of the obtained fault index.

S210: Based on the first fault index, the second fault index and the third fault index, determine a fault detection result corresponding to the single battery to be detected.

For the description of S210, reference may be made to the foregoing embodiments for details, and details are not repeated here.

In this embodiment, the first abnormal factor is determined based on the number of cells to be detected contained in the battery pack to be detected, and the ohmic internal resistance values corresponding to the plurality of cells to be detected, based on Determine the second abnormal factor based on the number of batteries and the polarization capacitance values corresponding to the plurality of single cells to be detected, and determine the third abnormal factor based on the number of batteries and the polarization resistance values corresponding to the plurality of single cells to be detected , thus, the adaptability between the abnormal factor calculation process and the personalized application scenario can be effectively improved, and the accuracy of the obtained abnormal factor can be effectively improved. By determining the first average value and the first sample standard deviation of a plurality of first abnormal factors, and based on the first abnormal factor, the first average value and the first sample standard deviation, the first fault indicator is calculated, and the multiple The second average value and the second sample standard deviation of the second abnormal factor, and based on the second abnormal factor, the second average value and the second sample standard deviation, calculate the second fault index, and determine the first multiple third abnormal factors The three average values and the third sample standard deviation are calculated based on the third abnormal factor, the third average value and the third sample standard deviation to obtain the third fault index, thereby effectively improving the description effect of the obtained fault index.

FIG. 3 is a schematic flowchart of a battery fault detection method proposed by another embodiment of the present disclosure.

As shown in Figure 3, the battery fault detection method includes:

S301: Obtain an equivalent circuit model of a single battery to be tested, wherein the single battery to be tested belongs to a battery pack to be tested, and the battery pack to be tested includes a plurality of single batteries to be tested.

S302: Identify model parameters in the equivalent circuit model, where the model parameters include ohmic internal resistance, polarization capacitance, and polarization resistance.

S303: Calculate the target abnormal factor corresponding to the model parameters, wherein the target abnormal factor includes: a first abnormal factor corresponding to the ohmic internal resistance value, a second abnormal factor corresponding to the polarization capacitance value, and a second abnormal factor corresponding to the polarization resistance value the third outlier factor.

S304: Calculate the target fault index corresponding to the target abnormal factor, wherein the target fault index includes: a first fault index corresponding to the first abnormal factor, a second fault index corresponding to the second abnormal factor, and a third abnormal factor Corresponding third failure indicator.

For descriptions of S301-S304, reference may be made to the above-mentioned embodiments, and details are not repeated here.

S305: Determine a fault critical value.

Among them, the failure critical value can be used to compare with the target failure index, so as to determine whether the battery has failed.

Optionally, in some embodiments, when determining the fault critical value, the fault detection requirement information of the single battery to be detected may be obtained, and the target confidence value is determined according to the fault detection requirement information, based on the number of batteries and the target confidence value, from The fault critical value is obtained from the preset critical value table, wherein the preset critical value table contains multiple reference critical values, and the fault critical value belongs to multiple reference critical values. Therefore, the corresponding The fault critical value can effectively improve the applicability of the obtained fault critical value, and can effectively improve the fault detection effect.

S306: Determine a first comparison result between the first fault index and the fault critical value.

Wherein, the first comparison result refers to the comparison result between the first fault index and the fault critical value, and may be used to indicate the magnitude relationship between the first fault index and the fault critical value.

S307: Determine a second comparison result between the second fault index and the fault critical value.

Wherein, the second comparison result refers to the comparison result between the second fault index and the fault critical value, and may be used to indicate the magnitude relationship between the second fault index and the fault critical value.

S308: Determine a third comparison result between the third failure index and the failure critical value.

Wherein, the third comparison result refers to the comparison result between the third fault index and the fault critical value, and may be used to indicate the magnitude relationship between the third fault index and the fault critical value.

S309: Determine 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 in response to the first comparison result, the second comparison result And the third comparison result satisfies the preset condition, and it is determined that the single battery to be detected has not failed, and in response to the first comparison result, the second comparison result, and the third comparison result not meeting the preset condition, it is determined that the unit to be detected Therefore, it can provide a reliable judgment basis for determining the failure of the single battery to be detected.

Wherein, the preset condition refers to the judgment condition configured in advance for the first comparison result, the second comparison result and the third comparison result.

Optionally, in some embodiments, the preset conditions include: the first comparison result is that the first failure index is less than the failure threshold; the second comparison result is that the second failure index is less than the failure threshold; the third comparison result is is that the third fault index is less than the fault critical value. That is to say, in the embodiment of the present disclosure, only when the first fault index, the second fault index and the third fault index corresponding to the single battery to be detected are all smaller than the fault critical value, it can be determined that the single battery to be detected is faulty. When any one of the first fault index, the second fault index and the third fault index is greater than or equal to the fault critical value, it can be determined that the single battery to be detected has a fault.

That is to say, in the embodiment of the present disclosure, after calculating the target fault index corresponding to the target abnormal factor, the fault critical value can be determined, the first comparison result between the first fault index and the fault critical value can be determined, and the second fault index can be determined Determine the third comparison result between the third fault index and the fault critical value based on the second comparison result with the fault critical value, and determine the fault detection result according to the first comparison result, the second comparison result, and the third comparison result , thus, multiple fault point detection of the single battery to be detected can be realized, and the reliability of battery fault detection can be effectively improved.

In this embodiment, by determining the fault critical value, the first comparison result between the first fault index and the fault critical value is determined, the second comparison result between the second fault index and the fault critical value is determined, and the third fault index and fault The third comparison result of the critical value, according to the first comparison result, the second comparison result and the third comparison result, determines the fault detection result, thus, it is possible to realize the detection of multiple fault points of the single battery to be detected, and it is possible to Effectively improve the reliability of battery fault detection. By obtaining the fault detection demand information of the single battery to be detected, according to the fault detection demand information, the target confidence value is determined, and based on the battery quantity and the target confidence value, the fault critical value is obtained from the preset critical value table, wherein the preset critical value The table contains multiple reference critical values, and the fault critical value belongs to multiple reference critical values. Therefore, the corresponding fault critical value can be flexibly configured for individualized application scenarios, thereby effectively improving the applicability of the obtained fault critical value and effectively Improve the fault detection effect. By responding to the first comparison result, the second comparison result and the third comparison result satisfying the preset condition, it is determined that the single battery to be detected has not failed, and in response to the first comparison result, the second comparison result and the third comparison result If the three-comparison result does not meet the preset condition, it is determined that the single battery to be tested is faulty, thereby providing a reliable judgment basis for determining that the single battery to be tested is faulty.

It can be understood that the local anomaly factor can also be called a local outlier factor. For example, as shown in FIG. 4 , FIG. 4 is a flowchart of a fault detection method using a local outlier factor proposed according to the present disclosure, wherein , U _k and I _K are the real-time voltage and current values of the single battery to be detected, U _ocv is the open circuit voltage, R _in is the ohmic internal resistance, R _p is the polarization resistance, and C _p is the polarization capacitance, U _t refers to the terminal voltage, y _k refers to the estimated voltage based on the battery model, X={R _in,i ,R _p,i ,C _p,i } refers to the A sample data set composed of ohmic internal resistance, polarization resistance and polarization capacitance, p refers to a sample data in the sample data set X, is the set of m nearest neighbors of p, /> Indicates the local reachability density of p, /> Indicates the reachability distance of p relative to o, /> is the Euclidean distance between p and o. /> is the m-distance of p, defined as the distance between o and its m-th nearest neighbor, is the outlier factor set, /> and /> Denote the fault indicators of R _in , R _p and C _p respectively. Grubbs criterion, which belongs to the branch of normal distribution, refers to the absolute value of the residual error of a measurement value |Vi|>Gg, then it is judged that there is a large error in this value and should be eliminated. An outlier filter based on Grubbs criterion is designed in Fig. 4 to check whether the outlier factor is within its normal range.

FIG. 5 is a schematic structural diagram of a battery fault detection device proposed by an embodiment of the present disclosure.

As shown in Figure 5, the battery failure detection device 50 includes:

An acquisition module 501, configured to acquire an equivalent circuit model of a single battery to be tested, wherein the single battery to be tested belongs to a battery pack to be tested, and the battery pack to be tested includes a plurality of single batteries to be tested;

An identification module 502, configured to identify model parameters in the equivalent circuit model, where the model parameters include ohmic internal resistance, polarization capacitance and polarization resistance;

The first calculation module 503 is used to calculate the target abnormal factor corresponding to the model parameters, wherein the target abnormal factor includes: a first abnormal factor corresponding to the ohmic internal resistance value, a second abnormal factor corresponding to the polarization capacitance value, and a third anomalous factor corresponding to the polarization resistance value;

The second calculation module 504 is configured to calculate a target failure indicator corresponding to the target abnormal factor, wherein the target failure indicator includes: a first failure indicator corresponding to the first abnormal factor, a second failure indicator corresponding to the second abnormal factor, and a third fault index corresponding to the third abnormal factor;

The determining module 505 is configured to determine 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.

It should be noted that, the foregoing explanations on the battery fault detection method are also applicable to the battery fault detection device of this embodiment, and will not be repeated here.

In this embodiment, by obtaining the equivalent circuit model of the single battery to be tested, wherein the single battery to be tested belongs to the battery pack to be tested, and the battery pack to be tested contains a plurality of single cells to be tested, and the equivalent circuit model is identified The model parameters in , where the model parameters include ohmic internal resistance, polarization capacitance and polarization resistance, calculate the target anomaly factor corresponding to the model parameters, wherein the target anomaly factor includes: the first ohmic internal resistance corresponding to An abnormal factor, a second abnormal factor corresponding to the polarization capacitance value, and a third abnormal factor corresponding to the polarization resistance value, calculate a target fault index corresponding to the target abnormal factor, wherein the target fault index includes: The first fault index corresponding to the abnormal factor, the second fault index corresponding to the second abnormal factor, and the third fault index corresponding to the third abnormal factor, based on the first fault index, the second fault index and the third fault index, By determining the fault detection result corresponding to the single battery to be detected, the accuracy and applicability of battery fault detection can be effectively improved, the reliability and safety of the energy storage battery system can be improved, and maintenance costs and time can be reduced.

FIG. 6 shows 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 only an example, and should not limit the functions and scope of use of the embodiments of the present disclosure.

As shown in FIG. 6, electronic device 12 takes the form of a general-purpose computing device. Components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16 , system memory 28 , bus 18 connecting various system components (including system memory 28 and processing unit 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, or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (Industry Standard Architecture; hereinafter referred to as: ISA) bus, Micro Channel Architecture (Micro Channel Architecture; hereinafter referred to as: MAC) bus, enhanced ISA bus, video electronics standard Association (Video Electronics Standards Association; hereinafter referred to as: VESA) local bus and peripheral component interconnection (Peripheral Component Interconnection; hereinafter referred to as: PCI) bus.

Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12 and include both volatile and nonvolatile media, removable and non-removable media.

The memory 28 may include a computer system readable medium in the form of a volatile memory, such as a random access memory (Random Access Memory; RAM for short) 30 and/or a 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 and write to non-removable, non-volatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive").

Although not shown in Figure 6, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk") may be provided, as well as a removable non-volatile disk (such as a Compact Disk ROM (Compact Disc Read Only Memory; hereinafter referred to as: CD-ROM), Digital Video Disc Read Only Memory (hereinafter referred to as: DVD-ROM) or other optical media) read and write optical disc drives. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the present disclosure.

Program/utility 40 may be stored, for example, in memory 28 as a set (at least one) of 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 these examples may include implementations of network environments. The program modules 42 generally perform the functions and/or methods of the embodiments described in this disclosure.

The electronic device 12 can also communicate with one or more external devices 14 (such as keyboards, pointing devices, displays 24, etc.), and can also communicate with one or more devices that allow the human body to interact with the electronic device 12, and/or communicate with Any device (eg, network card, modem, etc.) that enables the electronic device 12 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 22 . Moreover, the electronic device 12 can also be connected to one or more networks (such as a local area network (Local Area Network; hereinafter referred to as: LAN), a wide area network (Wide Area Network; hereinafter referred to as: WAN) and/or public networks, such as the Internet, through the network adapter 20. ) communication. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18 . It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.

The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28 , such as implementing the battery failure detection method mentioned in the foregoing embodiments.

In order to realize the above-mentioned embodiments, the present disclosure also proposes a non-transitory computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the battery failure detection method as proposed in the foregoing embodiments of the present disclosure is implemented.

In order to realize the above-mentioned embodiments, the present disclosure further proposes a computer program product. When the instruction processor in the computer program product executes, it executes the method for detecting battery failure as provided in the foregoing embodiments of the present disclosure.

Other embodiments of the disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. The present disclosure is intended to cover any modification, use or adaptation of the present disclosure. These modifications, uses or adaptations follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. . The specification and examples are to be considered exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It should be understood that the present disclosure is not limited to the precise constructions which have been described above and shown in the drawings, and various modifications and changes may be made 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, terms such as "first" and "second" are used for description purposes only, and should not be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, unless otherwise specified, "plurality" means two or more.

Any process or method descriptions in flowcharts or otherwise described herein may be understood to represent modules, segments or portions of code comprising one or more executable instructions for implementing specific logical functions or steps of the process , and the scope of preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order depending on the functions involved, which shall It is understood by those skilled in the art to which the embodiments of the present disclosure pertain.

It should be understood that various parts of the present disclosure may be implemented in hardware, software, firmware or a combination thereof. In the embodiments described above, various steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques known in the art: Discrete logic circuits, ASICs with suitable combinational logic gates, Programmable Gate Arrays (PGAs), Field Programmable Gate Arrays (FPGAs), etc.

Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. During execution, one or a combination of the steps of the method embodiments is included.

In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing module, each unit may exist separately physically, or two or more units may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules. If the integrated modules are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, and the like.

In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations on the present disclosure, and those skilled in the art can understand the above-mentioned embodiments within the scope of the present disclosure. The embodiments are subject to changes, modifications, substitutions and variations.

Claims (7)

1. A battery fault detection method, characterized in that, comprising: Obtaining an equivalent circuit model of a single battery to be tested, wherein the single battery to be tested belongs to a battery pack to be tested, and the battery pack to be tested includes a plurality of single batteries to be tested; Identifying model parameters in the equivalent circuit model, wherein the model parameters include ohmic internal resistance, polarization capacitance and polarization resistance; calculating a target anomaly factor corresponding to the model parameter, wherein the target anomaly factor includes: a first anomalous factor corresponding to the ohmic internal resistance value, a second anomalous factor corresponding to the polarization capacitance value, and The third abnormal factor corresponding to the polarization resistance value, wherein the target abnormal factor is an abnormal factor corresponding to the model parameter calculated based on the local abnormal factor method, the larger the value of the target abnormal factor, the corresponding model parameter belongs to The probability of failure of the single battery to be detected is also greater; calculating a target fault index corresponding to the target abnormal factor, wherein the target fault index includes: 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; 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 calculation of the target failure index corresponding to the target abnormal factor includes: Determining a first average value and a first sample standard deviation of a plurality of first abnormal factors, and based on the first abnormal factor, the first average value and the first sample standard deviation, calculating the the first failure indicator; Determining a second average value and a second sample standard deviation of a plurality of second abnormal factors, and calculating the first Two failure indicators; determining a third average value and a third sample standard deviation of a plurality of third abnormal factors, and calculating the third abnormal factor based on the third abnormal factor, the third average value, and the third sample standard deviation Three failure indicators: determining a failure detection result corresponding to the single battery to be detected based on the first failure indicator, the second failure indicator and the third failure indicator includes: Determining the fault critical value, the determining the fault critical value 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; Based on the number of batteries and the target confidence value, the fault critical value is obtained from a preset critical value table, wherein the preset critical value table contains a plurality of reference critical values, and the fault critical value belongs to the plurality of reference critical values. a reference threshold; determining a first comparison result between the first fault index and the fault critical value; determining a second comparison result between the second fault index and the fault critical value; determining a third comparison result between the third fault index and the fault critical value; The fault detection result is determined according to the first comparison result, the second comparison result, and the third comparison result. 2. The method according to claim 1, wherein the calculation of the target anomaly factor corresponding to the model parameters comprises: Determining the battery quantity of the single battery to be tested contained in the battery pack to be tested; determining the first abnormality factor based on the number of batteries and the ohmic internal resistance values corresponding to the plurality of single batteries to be tested; determining the second abnormality factor based on the number of batteries and the polarization capacitance values corresponding to the plurality of single batteries to be detected; The third abnormality factor is determined based on the battery quantity and the polarization resistance values corresponding to the plurality of single batteries to be detected. 3. The method according to claim 1, wherein the determining the fault detection result according to the first comparison result, the second comparison result and the third comparison result comprises : In response to the first comparison result, the second comparison result, and the third comparison result satisfying a preset condition, it is determined that the single battery to be detected is not faulty; In response to the first comparison result, the second comparison result and the third comparison result not satisfying the preset condition, it is determined that the single battery to be detected is faulty. 4. The method according to claim 3, wherein the preset conditions include: The first comparison result is that the first failure index is less than the failure threshold; The second comparison result is that the second failure index is less than the failure threshold; The third comparison result is that the third failure index is smaller than the failure threshold. 5. A battery fault detection device, characterized in that, comprising: An acquisition module, configured to acquire an equivalent circuit model of a single battery to be tested, wherein the single battery to be tested belongs to a battery pack to be tested, and the battery pack to be tested includes a plurality of single batteries to be tested; An identification module, configured to identify model parameters in the equivalent circuit model, wherein the model parameters include ohmic internal resistance, polarization capacitance and polarization resistance; The first calculation module is used to calculate the target abnormal factor corresponding to the model parameter, wherein the target abnormal factor includes: a first abnormal factor corresponding to the ohmic internal resistance value, a first abnormal factor corresponding to the polarization capacitance value The second anomalous factor of , and the third anomalous factor corresponding to the polarization resistance value, wherein the target anomalous factor is the anomalous factor corresponding to the model parameters calculated based on the local anomalous factor method, and the value of the target anomalous factor The larger the value, the greater the probability of the failure of the single battery to be detected to which the corresponding model parameter belongs; The second calculation module is used to calculate the target fault index corresponding to the target abnormal factor, wherein the target fault index includes: the first fault index corresponding to the first abnormal factor, and the second abnormal factor a corresponding second fault index, and a third fault index corresponding to the third abnormal factor; A determining module, configured to determine 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 also used for the calculation of the target failure index corresponding to the target abnormal factor, including: Determining a first average value and a first sample standard deviation of a plurality of first abnormal factors, and based on the first abnormal factor, the first average value and the first sample standard deviation, calculating the the first failure indicator; Determining a second average value and a second sample standard deviation of a plurality of second abnormal factors, and calculating the first Two failure indicators; determining a third average value and a third sample standard deviation of a plurality of third abnormal factors, and calculating the third abnormal factor based on the third abnormal factor, the third average value, and the third sample standard deviation Three failure indicators; The determination module is also used to determine the fault critical value, and the determination of the fault critical value 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; Based on the number of batteries and the target confidence value, the fault critical value is obtained from a preset critical value table, wherein the preset critical value table contains a plurality of reference critical values, and the fault critical value belongs to the plurality of reference critical values. a reference threshold; determining a first comparison result between the first fault index and the fault critical value; determining a second comparison result between the second fault index and the fault critical value; determining a third comparison result between the third fault index and the fault critical value; The fault detection result is determined according to the first comparison result, the second comparison result, and the third comparison result. 6. An electronic device, characterized in that it comprises: 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, and the instructions are executed by the at least one processor, so that the at least one processor can perform any one of claims 1-4. Methods. 7. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to make the computer execute the method according to any one of claims 1-4.