CN112530049B - Battery state monitoring method and device, electronic device and storage medium - Google Patents

Abstract

The battery state monitoring method and device, electronic device and storage medium provided by the present application relate to the technical field of battery state monitoring. In the present application, current time integrals in at least one dimension are obtained based on historical charging data, secondly, current time integrals are clustered based on each dimension, and then capacity increment curves are formed based on different types of current time integrals, respectively, This makes it possible to determine the target capacity deviation value based on the capacity increment curve of the corresponding type of the target charging data, so as to determine the state information of the power supply device based on the target capacity deviation value. Based on the above method, the problem in the prior art that it is difficult to effectively monitor the battery state of the power supply device can be improved.

Description

Battery state monitoring method and device, electronic device and storage medium

technical field

The present application relates to the technical field of battery state monitoring, and in particular, to a battery state monitoring method and device, an electronic device, and a storage medium.

Background technique

The power supply equipment formed based on rechargeable and dischargeable batteries is widely used in the fields of electric vehicles and energy storage, making its safe operation an important part of the use process, that is, how to detect the abnormal state of the battery timely and accurately through the monitoring data, which is the key to the battery It is an important link in the large-scale safe operation of the system.

Among them, the existing battery abnormal identification scheme is mainly based on the battery equivalent circuit model and the battery electrochemical mechanism model.

However, the above two methods both rely on high-precision and high-frequency data collection, and require a large amount of calculation. For large-scale data applications, for example, when monitoring the current million-level operating battery system, the calculation cannot be implemented, and the current monitoring platform, single-unit sampling accuracy or sampling frequency cannot make the equivalent circuit model based Therefore, it is difficult to effectively monitor the battery state of the power supply equipment.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present application is to provide a battery state monitoring method and device, an electronic device and a storage medium, so as to improve the problem existing in the prior art that it is difficult to effectively monitor the battery state of a power supply device.

To achieve the above purpose, the embodiment of the present application adopts the following technical solutions:

A battery state monitoring method is used to monitor the state of a power supply device, wherein the power supply device includes a plurality of single cells, and the method includes:

Obtain at least one piece of historical charging data formed by charging the power supply equipment in the past and at least one piece of target charging data formed by current charging, wherein each piece of historical charging data includes the plurality of single cells being charged at the same time the historical charging voltage at the time, each piece of the target charging data includes the target charging voltage of the plurality of single cells at the same charging time;

For each piece of the historical charging data, obtain the current time integral of the average charging voltage corresponding to the historical charging data in each preset dimension, wherein the average charging voltage is based on a plurality of historical charging data included in the corresponding historical charging data voltage is obtained, the dimension is at least one;

In the feature space formed based on at least one of the dimensions, the obtained current time integrals are clustered to obtain at least one integral category, wherein any one of the integral categories includes a current corresponding to each average charging voltage time integral;

determining a target point category in the at least one point category based on the dimension information of the at least one piece of target charging data in at least one of the dimensions;

For each of the charging average voltages, based on the capacity increment curve corresponding to the target integral category, calculate the target capacity deviation value between the two target charging voltages corresponding to the charging average voltage at the charging time, wherein the capacity The incremental curve is formed based on the current time integration corresponding to each charging average voltage included in the target integration category;

The state information of the power supply device is determined based on the target capacity deviation value.

In a preferred option of the embodiment of the present application, in the above battery state monitoring method, for each piece of the historical charging data, the current in each preset dimension of the average charging voltage corresponding to the historical charging data is obtained. The steps of time integration include:

For each piece of the historical charging data, perform an average value calculation process based on a plurality of historical charging voltages included in the historical charging data to obtain a corresponding average charging voltage;

For each of the charging average voltages, a voltage range is determined based on the charging average voltage, wherein each of the charging average voltages belongs to a corresponding voltage range, and any two voltage ranges do not overlap when there are multiple voltage ranges;

For each of the voltage ranges, calculate the capacity change between the voltage upper limit value and the voltage lower limit value in each predetermined dimension in the voltage range, and use the capacity change as the charge corresponding to the voltage range Current time integral of the average voltage.

In a preferred choice of the embodiment of the present application, in the above-mentioned battery state monitoring method, for each piece of the historical charging data, an average value calculation process is performed based on a plurality of historical charging voltages included in the historical charging data to obtain the corresponding The steps of charging the average voltage include:

For each piece of the historical charging data, perform probability distribution statistical processing on a plurality of historical charging voltages included in the historical charging data to obtain corresponding probability distribution information;

For each of the probability distribution information, based on the probability distribution information among the corresponding plurality of historical charging voltages, screen out at least one historical charging voltage whose positive and negative deviations are within the standard deviation range of a preset multiple;

For each of the probability distribution information, an average value of the at least one historical charging voltage corresponding to the probability distribution information is calculated, and the average value is used as the charging average voltage of the historical charging data corresponding to the probability distribution information.

In a preferred option of the embodiment of the present application, in the above-mentioned battery state monitoring method, for each of the voltage ranges, the upper voltage limit and the lower voltage limit in the voltage range are calculated in each predetermined dimension. Steps for the amount of change in capacity between values, including:

Determine at least one dimension in the dimension of temperature value, the dimension of current value, the dimension of the difference between the moments before and after the current change, and the dimension of the accumulated charging power value;

For each of the voltage ranges, the capacity variation between the voltage upper limit value and the voltage lower limit value in the voltage range is calculated in each of the at least one dimension, respectively.

In a preferred option of the embodiment of the present application, in the above-mentioned battery state monitoring method, for each of the charging average voltages, based on the capacity increment curve corresponding to the target integral category, calculate the charging average voltage during charging. The steps of the target capacity deviation value between the two corresponding target charging voltages at the moment include:

For each of the average charging voltages, among a plurality of target charging voltages at the time corresponding to the average charging voltage, determine a maximum target charging voltage and a minimum target charging voltage;

For each of the charging average voltages, based on the capacity increment curve corresponding to the target integral category, a target capacity deviation value between the maximum target charging voltage and the minimum target charging voltage corresponding to the charging average voltage is calculated.

In a preferred option of the embodiment of the present application, in the above battery state monitoring method, the step of determining the state information of the power supply device based on the target capacity deviation value includes:

calculating a capacity deviation rate based on the target capacity deviation value and the historical capacity deviation value corresponding to the historical charging data;

Based on the target capacity deviation value and the capacity deviation rate, state information of the power supply device is determined, wherein the state information includes whether the power supply device is in an abnormal state.

In a preferred option of the embodiment of the present application, in the above battery state monitoring method, the step of determining the state information of the power supply device based on the target capacity deviation value and the capacity deviation rate includes:

Obtaining a predetermined first weight coefficient and a second weight coefficient, wherein the first weight coefficient is generated based on the configuration of the target capacity deviation value, and the second weight coefficient is generated based on the configuration of the capacity deviation rate ;

Based on the first weight coefficient, the second weight coefficient, the target capacity deviation value, the capacity deviation rate, and a predetermined determination threshold, the state information of the power supply device is determined.

The embodiment of the present application also provides a battery state monitoring device for monitoring the state of a power supply device, wherein the power supply device includes a plurality of single cells, and the device includes:

A data acquisition module, configured to acquire at least one piece of historical charging data formed by historical charging of the power supply device and at least one piece of target charging data formed by current charging, wherein each piece of historical charging data includes the plurality of the historical charging voltages of the single cells at the same charging moment, and each piece of the target charging data includes the target charging voltages of the plurality of single cells at the same charging moment;

The integral acquisition module is configured to, for each piece of historical charging data, acquire the current time integral of the average charging voltage corresponding to the historical charging data in each preset dimension, wherein the average charging voltage is based on the corresponding historical charging data The included multiple historical charging voltages are obtained, and the dimension is at least one;

A clustering processing module, configured to perform clustering processing on the obtained current time integrals in the feature space formed based on at least one of the dimensions to obtain at least one integral category, wherein any integral category includes each charging average A current time integral corresponding to the voltage;

a category determination module, configured to determine a target point category in the at least one point category based on the dimension information of the at least one piece of target charging data in at least one of the dimensions;

A deviation value calculation module, configured to calculate, for each of the average charging voltages, a target capacity between two target charging voltages corresponding to the average charging voltage at the charging time based on the capacity increment curve corresponding to the target integral category a deviation value, wherein the capacity increment curve is formed based on the current time integration corresponding to each charging average voltage included in the target integration category;

A state determination module, configured to determine the state information of the power supply device based on the target capacity deviation value.

On the above basis, the embodiment of the present application also provides an electronic device, including:

memory for storing computer programs;

The processor connected with the memory is used for executing the computer program stored in the memory, so as to realize the above-mentioned battery state monitoring method.

Based on the above, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed, the above-mentioned battery state monitoring method is implemented.

In the battery state monitoring method and device, electronic device and storage medium provided by the present application, firstly, the time integral of current (charging current) in at least one dimension can be obtained based on historical charging data, and secondly, the time integral of current can be obtained based on each dimension Clustering is performed, and then capacity increment curves can be formed based on the current time integrals of different types, so that the target capacity deviation value can be determined based on the capacity increment curve of the corresponding type of the target charging data, so that the power supply equipment can be determined based on the target capacity deviation value. status information. Based on this, the state of the power supply equipment can be effectively determined, so as to improve the problem that it is difficult to effectively monitor the battery state of the power supply equipment in the prior art, reduce the probability of a safety accident during the operation of the power supply equipment, and ensure that The safe operation of the power supply equipment itself and the application environment makes it of high practical value.

In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the preferred embodiments are exemplified below, and are described in detail as follows in conjunction with the accompanying drawings.

Description of drawings

FIG. 1 is a structural block diagram of an electronic device provided by an embodiment of the present application.

FIG. 2 is a schematic flowchart of a battery state monitoring method provided by an embodiment of the present application.

FIG. 3 is a schematic flowchart of sub-steps included in step S120 in FIG. 2 .

FIG. 4 is a schematic flowchart of sub-steps included in step S121 in FIG. 3 .

FIG. 5 is a schematic flowchart of sub-steps included in step S123 in FIG. 3 .

FIG. 6 is a schematic flowchart of sub-steps included in step S150 in FIG. 2 .

FIG. 7 is a schematic diagram of a capacity increment curve provided by an embodiment of the present application.

FIG. 8 is a schematic flowchart of sub-steps included in step S160 in FIG. 2 .

FIG. 9 is a schematic flowchart of sub-steps included in step S162 in FIG. 8 .

FIG. 10 is a schematic block diagram of a battery state monitoring apparatus provided by an embodiment of the present application.

Icon: 10-electronic equipment; 12-memory; 14-processor; 100-battery state monitoring device; 110-data acquisition module; 120-integration acquisition module; 130-cluster processing module; 140-category determination module; 150- Deviation value calculation module; 160-state determination module.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments It is only a part of the embodiments of the present application, but not all of the embodiments. The components of the embodiments of the present application generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations.

Thus, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the application as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

As shown in FIG. 1 , an embodiment of the present application provides an electronic device 10 , which may include a memory 12 , a processor 14 and a battery state monitoring apparatus 100 .

Wherein, the memory 12 and the processor 14 are directly or indirectly electrically connected to realize data transmission or interaction. For example, they can be electrically connected to each other through one or more communication buses or signal lines. The battery state monitoring device 100 includes at least one software function module that can be stored in the memory 12 in the form of software or firmware. The processor 14 is configured to execute executable computer programs stored in the memory 12, for example, software function modules and computer programs included in the battery state monitoring device 100, so as to implement the battery provided by the embodiments of the present application. Condition monitoring methods (described later).

Optionally, the memory 12 may be, but not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read only memory (Programmable Read-Only Memory, PROM) , Erasable Programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), Electrically Erasable Programmable Read-Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

Moreover, the processor 14 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), and the like.

It can be understood that the structure shown in FIG. 1 is only for illustration, and the electronic device 10 may further include more or less components than those shown in FIG. 1 , or have different configurations than those shown in FIG. 1 . For example, the electronic device 10 may also include a communication unit for information interaction with other devices.

With reference to FIG. 2 , an embodiment of the present application further provides a battery state monitoring method applicable to the electronic device 10 described above, for monitoring the state of the power supply device. The method steps defined by the related flow of the battery state monitoring method may be implemented by the electronic device 10 .

The specific flow shown in FIG. 2 will be described in detail below.

Step S110: Acquire at least one piece of historical charging data formed by charging the power supply equipment in the past and at least one piece of target charging data formed by current charging.

In this embodiment, the electronic device 10 can acquire at least one piece of historical charging data formed by charging the power supply device in the past, and acquire at least one piece of target charging data formed by the current charging of the power supply device.

Wherein, the power supply device includes a plurality of single cells, and each piece of the historical charging data includes the historical charging voltages of the plurality of single cells at the same charging moment (in this way, at one charging moment, a plurality of historical charging voltages can be obtained ), each piece of the target charging data includes target charging voltages of the plurality of single cells at the same charging moment (in this way, at one charging moment, multiple target charging voltages can be obtained).

Step S120: For each piece of the historical charging data, obtain the current time integral of the average charging voltage corresponding to the historical charging data in each preset dimension.

In this embodiment, after obtaining the at least one piece of historical charging data based on step S110, the electronic device 10 may, for each piece of historical charging data, obtain the average charging voltage corresponding to the historical charging data in each preset The time integral of the current over the dimension.

The average charging voltage is obtained based on a plurality of historical charging voltages (ie, historical charging voltages of a plurality of single cells) included in the corresponding historical charging data, and the dimension is at least one.

Step S130, in the feature space formed based on at least one of the dimensions, perform clustering processing on the obtained current time integrals to obtain at least one integral category.

In this embodiment, after obtaining the current time integration of the average charging voltage corresponding to each piece of historical charging data in each dimension based on step S120, the electronic device 10 can perform a feature space based on the at least one dimension. In the process, cluster processing is performed on the obtained current time integrals, so that at least one integral category can be obtained.

Wherein, any one of the integration categories may include a current time integration corresponding to each charging average voltage.

Step S140, based on the dimension information of the at least one piece of target charging data in at least one of the dimensions, determine a target point category in the at least one point category.

In this embodiment, after the at least one piece of target charging data is acquired based on step S110 and the at least one point category is acquired based on step S130, the electronic device 10 may Dimensional information on at least one dimension in which a target score category is determined.

Step S150 , for each of the average charging voltages, based on the capacity increment curve corresponding to the target integral category, calculate the target capacity deviation value between the two target charging voltages corresponding to the average charging voltage at the charging time.

In this embodiment, after determining the target integration category based on step S140, the electronic device 10 may, for each of the charging average voltages, calculate the charging average voltage based on the capacity increment curve corresponding to the target integration category The target capacity deviation value between the two corresponding target charging voltages at the charging time (that is, the charging time of the average charging voltage is the same as the charging time of the two target charging voltages, wherein each charging can form at least one charging time) .

Wherein, the capacity increment curve may be formed based on the current time integration corresponding to each charging average voltage included in the target integration category.

Step S160, determining the state information of the power supply equipment based on the target capacity deviation value.

In this embodiment, after obtaining the target capacity deviation value based on step S160, the electronic device 10 may determine the state information of the power supply device based on the target capacity deviation value.

Based on the above method, the status information of the power supply equipment can be effectively determined, thereby improving the problem of difficulty in effectively monitoring the battery status of the power supply equipment in the prior art, and reducing the probability of a safety accident during the operation of the power supply equipment.

In the first aspect, it should be noted for step S110 that the specific manner of acquiring the at least one piece of historical charging data is not limited, and can be selected according to actual application requirements.

For example, in an alternative example, the data obtained by performing data collection on the power supply device (for example, collected by a corresponding sensor) can be directly used as the historical charging data, wherein based on different charging At different times, different historical charging data can be obtained.

For another example, in another alternative example, the data obtained by performing data collection on the power supply device (for example, collected by a corresponding sensor) may be cleaned first, for example, setting a corresponding threshold, so as to prevent the data from exceeding the threshold. The data is deleted, so as to obtain the historical charging data.

For another example, in another alternative example, the data obtained by performing data collection on the power supply device (for example, collected by a corresponding sensor), considering that the problem of loss may occur in the process of sending or storing, Therefore, interpolation processing, such as nearest neighbor interpolation, linear interpolation, spline function interpolation or piecewise multiple interpolation, etc., can be performed, so as to obtain the historical charging data.

And, in another alternative example, the data obtained by data collection (such as collection by a corresponding sensor) on the power supply equipment, considering that the collection process may be due to interference of the equipment or due to the sampling frequency itself. Therefore, filtering processing, such as mean filtering, median filtering or wavelet filtering, can be performed to obtain the historical charging data.

In the second aspect, it should be noted that in step S120, the specific manner of obtaining the current time integral is not limited, and can be selected according to actual application requirements.

For example, in an alternative example, with reference to FIG. 3 , step S120 may include step S121 , step S122 and step S123 , the specific contents of which are as follows.

Step S121, for each piece of the historical charging data, perform an average value calculation process based on a plurality of historical charging voltages included in the historical charging data to obtain a corresponding average charging voltage.

In this embodiment, after the at least one piece of historical charging data is acquired based on step S110, an average value calculation process may be performed for each piece of historical charging data based on a plurality of historical charging voltages included in the historical charging data. In this way, it is possible to obtain The corresponding charging average voltage.

That is to say, a charging moment may form a piece of historical charging data, and the historical charging data may include the historical charging voltage of each single cell at the charging moment, that is, for multiple single cells, there may be multiple historical charging voltages.

Step S122, for each of the charging average voltages, determine a voltage range based on the charging average voltage.

In this embodiment, after obtaining at least one average charging voltage based on step S121, a voltage range may be determined based on the average charging voltage (for example, if a plurality of average charging voltages are obtained, the voltage range may be determined based on the plurality of average charging voltages) division).

Wherein, each of the charging average voltages belongs to a corresponding voltage range, and any two voltage ranges do not overlap when there are multiple voltage ranges.

Step S123, for each of the voltage ranges, calculate the capacity change between the voltage upper limit value and the voltage lower limit value in the voltage range on each predetermined dimension, and use the capacity change as the voltage range The current time integral of the corresponding charge average voltage.

In this embodiment, after the voltage range of each of the charging average voltages is determined based on step S122, the capacity change between the voltage upper limit value and the voltage lower limit value in the voltage range is calculated in each predetermined dimension (such as the difference between the capacity corresponding to the upper voltage limit value and the capacity corresponding to the lower voltage limit value), and the capacity change amount is taken as the current time integration of the average charging voltage corresponding to the voltage range.

That is to say, if the average charging voltage is 3 and the dimension is 3, the obtained current time integral may be 3*3*3=27.

Optionally, in the above example, the specific manner of performing the mean value calculation processing based on step S121 is not limited, and can be selected according to actual application requirements.

For example, in an alternative example, in order to make the obtained average charging voltage better represent the overall information of multiple single cells, with reference to FIG. 4 , step S121 may include step S121a, step S121b and step S121c, specifically The contents are as follows.

Step S121a, for each piece of the historical charging data, perform probability distribution statistical processing on a plurality of historical charging voltages included in the historical charging data to obtain corresponding probability distribution information.

In this embodiment, after the at least one piece of historical charging data is acquired based on step S110, probability distribution statistics processing may be performed on a plurality of historical charging voltages included in the historical charging data for each piece of historical charging data. Get the corresponding probability distribution information.

Step S121b, for each of the probability distribution information, from the corresponding plurality of historical charging voltages, based on the probability distribution information, screen out at least one historical charging voltage whose positive and negative deviations are within the standard deviation range of a preset multiple.

In this embodiment, after the probability distribution information is obtained based on step S121a, for each probability distribution information, based on the probability distribution information, the positive and negative deviations may be selected as preset multiples from the corresponding multiple historical charging voltages (The preset multiple may be determined based on calculation amount and accuracy requirements, wherein, in an alternative example, the preset multiple may be 1) at least one historical charging voltage within the standard deviation range.

Step S121c, for each of the probability distribution information, calculate the average value of the at least one historical charging voltage corresponding to the probability distribution information, and use the average value as the charging average voltage of the historical charging data corresponding to the probability distribution information.

In this embodiment, after screening out at least one historical charging voltage for each of the probability distribution information based on step S121b, for each of the probability distribution information, the at least one historical charging voltage corresponding to the probability distribution information may be average of. Then, the average value can be used as the charging average voltage of the historical charging data corresponding to the probability probability distribution information.

Optionally, in the above example, the specific manner of calculating the capacity change amount in each dimension based on step S123 is not limited, and can be selected according to actual application requirements.

For example, in an alternative example, in order to improve the reliability of the obtained capacity change rate (ie, the current time integral), with reference to FIG. 5 , step S123 may include step S123a and step S123b, the details of which are as follows.

Step S123a, at least one dimension is determined from the dimension of temperature value, the dimension of current value, the dimension of the difference between the time before and after the current changes, and the dimension of the accumulated charging power value.

In this embodiment, after determining the voltage range of each of the average charging voltages based on step S122, at least one of the dimensions of the temperature value, the current value, the difference between moments before and after current changes, and the dimension of the accumulated charging power value may be determined at least One dimension (wherein, based on different precision requirements, different numbers of dimensions can be selected, for example, for higher precision requirements, more dimensions can be selected, so that the calculation basis is more sufficient).

Step S123b, for each voltage range, calculate the capacity variation between the voltage upper limit value and the voltage lower limit value in each of the determined at least one dimension in the voltage range respectively.

In this embodiment, after the at least one dimension is determined based on step S123a, for each voltage range, the difference between the voltage upper limit value and the voltage lower limit value in the voltage range may be calculated on each of the at least one dimension. capacity change between.

That is to say, if the average charging voltages are 3, the corresponding voltage ranges are 3, and the dimensions are 3, the obtained capacity changes may be 3*3*3=27.

In the third aspect, it should be noted for step S130 that the specific manner of performing the clustering processing on the obtained current time integral is not limited, and can be selected according to actual application requirements.

For example, in an alternative example, based on the K-MEANS algorithm, the K-MEDOIDS algorithm or the CLARANS algorithm, cluster processing is performed on the obtained at least one current time integral in the feature control, so that at least one current time integral can be obtained. Points category.

For another example, in another alternative example, based on the BIRCH algorithm, the CURE algorithm or the CHAMELEON algorithm, cluster processing is performed on the obtained at least one current time integral in the feature control, so that at least one integral category can be obtained. .

For another example, in another alternative example, based on the DBSCAN algorithm, the OPTICS algorithm or the DENCLUE algorithm, cluster processing is performed on the obtained at least one current time integral in the feature control, so that at least one integral category can be obtained. .

In the fourth aspect, it should be noted for step S140 that the specific manner of determining the target point category is not limited, and can be selected according to actual application requirements.

For example, in an alternative example, the dimension information of the at least one piece of target charging data in each dimension, such as temperature information, current information, etc., may be obtained first, and then, based on the dimension information, the K nearest neighbors (k -Nearest Neighbor, KNN) and other classification algorithms to determine a target integral class in the at least one integral class.

In the fifth aspect, it should be noted for step S150 that the specific method for calculating the target capacity deviation value is not limited, and can be selected according to actual application requirements.

For example, in an alternative example, in order to enable the obtained target capacity deviation value to effectively reflect the difference between the plurality of single cells, with reference to FIG. 6 , step S150 may include steps S151 and S152, specifically The contents are as follows.

Step S151 , for each of the average charging voltages, among a plurality of target charging voltages at the time corresponding to the average charging voltage, determine a maximum target charging voltage and a minimum target charging voltage.

In this embodiment, after obtaining the average charging voltage corresponding to each piece of the historical charging data, the maximum target charging voltage and the minimum target charging voltage (positive and Negative deviation from the maximum two target charging voltages).

That is to say, if the average charging voltage is 3.5V, then, for the at least one piece of target charging data, the voltage average value of multiple target charging voltages corresponding to each piece of target charging data can be determined first, and then the voltage average value can be determined as 3.5V target charging data, and then determine the maximum target charging voltage and the minimum target charging voltage from the multiple target charging voltages corresponding to the target charging data. In this way, for each of the average charging voltages, a corresponding maximum target charging voltage and a corresponding minimum target charging voltage can be obtained.

Step S152 , for each of the average charging voltages, based on the capacity increment curve corresponding to the target integral category, calculate a target capacity deviation value between the maximum target charging voltage and the minimum target charging voltage corresponding to the average charging voltage.

In this embodiment, after determining the maximum target charging voltage and the minimum target charging voltage corresponding to each of the average charging voltages based on step S151, the capacity increment curve corresponding to the target integral category obtained in step S140 (the The capacity increment curve is composed of the current time integral included in the target integral category, as shown in Figure 7), and the target capacity deviation value between the maximum target charging voltage and the minimum target charging voltage corresponding to each of the charging average voltages is calculated.

For example, in the example shown in FIG. 7 , if the maximum target charging voltage is 3.8V and the minimum target charging voltage is 3.6V, then the capacity increase curve, abscissa axis, and abscissa=3.6 are calculated. The area enclosed by the straight line of V and the straight line of abscissa=3.8V is used as the target capacity deviation value.

In the sixth aspect, it should be noted for step S160 that the specific manner of determining the state information of the power supply device is not limited, and can be selected according to actual application requirements.

For example, in an alternative example, in order to improve the accuracy of the determined state information, with reference to FIG. 8 , step S160 may include step S161 and step S162 , the details of which are as follows.

Step S161: Calculate a capacity deviation rate based on the target capacity deviation value and the historical capacity deviation value corresponding to the historical charging data.

In this embodiment, after the target capacity deviation value is obtained based on step S150, the historical capacity deviation value corresponding to the historical charging data can also be obtained (corresponding to different historical stages, such as the first charging, the second charging There can be multiple historical capacity deviation values for charging, the third charging, the fourth charging, etc.), so, based on the target capacity deviation value and the historical capacity deviation value, the capacity deviation rate (that is, the capacity deviation change rate) can be calculated. ).

Step S162: Determine the status information of the power supply device based on the target capacity deviation value and the capacity deviation rate.

In this embodiment, after the capacity offset rate is obtained based on step S161, the state information of the power supply equipment may be determined together with the capacity offset and the target capacity offset value (due to the determination of the As the basis or dimension increases, the state information can be made more reliable).

Wherein, the state information includes whether the power supply device is in an abnormal state.

It can be understood that when there are multiple target capacity deviation values, that is, target capacity deviation values corresponding to multiple charging moments, the target capacity deviation value and historical capacity deviation corresponding to the charging moment can be calculated for each charging moment. The value corresponds to the capacity deviation rate. In this way, the state information of the power supply equipment can be determined more reliably through the target capacity deviation values and capacity deviation rates corresponding to multiple charging times.

Optionally, the specific manner of determining the state information of the power supply device based on step S162 is not limited, and may be selected according to actual application requirements.

For example, in an alternative example, in order to meet different requirements, with reference to FIG. 9 , step S162 may include step S162a and step S162b, and the specific contents are as follows.

Step S162a, obtaining a predetermined first weight coefficient and a second weight coefficient.

In this embodiment, after obtaining the capacity offset rate based on step S161, a first weight coefficient and a second weight coefficient that are configured and generated in advance for the capacity offset rate and the target capacity offset value, respectively, may be obtained.

Wherein, the first weight coefficient is generated based on the configuration of the target capacity deviation value, and the second weight coefficient is generated based on the configuration of the capacity deviation rate (wherein, according to the actual demand, more emphasis is placed on the target capacity When it deviates from the value, the first weighting coefficient may be larger than the second weighting coefficient; when more emphasis is placed on the capacity offset rate according to actual requirements, the first weighting coefficient may be smaller than the second weighting coefficient).

Step S162b: Determine the state information of the power supply equipment based on the first weight coefficient, the second weight coefficient, the target capacity deviation value, the capacity deviation rate, and a predetermined determination threshold.

In this embodiment, after the first weighting coefficient and the second weighting coefficient are obtained based on step S162a, based on the first weighting coefficient and the second weighting coefficient, the target capacity deviation value and the Perform weighted calculation on the capacity offset rate (considering that the target capacity offset value and the capacity offset rate belong to parameters of different dimensions, normalization can be performed first, and then weighted calculation is performed), and then based on the obtained The weighted value and the predetermined determination threshold are used to determine the state information of the power supply device (for example, if the weighted value is greater than the determination threshold, the state information may indicate that the power supply device is in an abnormal state).

It should be noted that, when step S160 is executed, if the state information of the power supply device needs to be determined based on the change trend of the target capacity deviation value, the historical target capacity deviation value for which charging has been performed multiple times in the history can be obtained. In this way, the change trend of the capacity deviation value based on time can be obtained, or when the power supply device is applied to the electric vehicle, the change trend of the capacity deviation value based on the mileage can also be obtained. In addition, in order to improve the reliability of determining the state information of the power supply equipment based on the change trend, the capacity deviation value change trend based on time or mileage may also be subjected to capacity deviation value interpolation (such as an interpolation method such as linear interpolation).

With reference to FIG. 10 , an embodiment of the present application further provides a battery state monitoring apparatus 100 that can be applied to the electronic device 10 described above. The battery state monitoring device 100 may include a data acquisition module 110 , an integral acquisition module 120 , a clustering processing module 130 , a category determination module 140 , a deviation value calculation module 150 and a state determination module 160 .

The data acquisition module 110 is configured to acquire at least one piece of historical charging data formed by charging the power supply equipment in the past and at least one piece of target charging data formed by charging at the current time, wherein each piece of historical charging data includes all the pieces of historical charging data. The historical charging voltages of the plurality of single cells at the same charging moment, and each piece of the target charging data includes the target charging voltages of the plurality of single cells at the same charging moment. In this embodiment, the data acquisition module 110 may be configured to perform step S110 shown in FIG. 2 , and for the relevant content of the data acquisition module 110 , reference may be made to the foregoing description of step S110 .

The integral acquisition module 120 is configured to, for each piece of the historical charging data, acquire the current time integral of the average charging voltage corresponding to the historical charging data in each preset dimension, wherein the average charging voltage is based on the corresponding A plurality of historical charging voltages included in the historical charging data are obtained, and the dimension is at least one. In this embodiment, the point acquisition module 120 may be configured to execute step S120 shown in FIG. 2 , and for the relevant content of the point acquisition module 120 , reference may be made to the foregoing description of step S120 .

The clustering processing module 130 is configured to perform clustering processing on the obtained current time integrals in the feature space formed based on at least one of the dimensions to obtain at least one integral category, wherein any integral category includes each One current time integral for one charging average voltage. In this embodiment, the clustering processing module 130 may be configured to execute the step S130 shown in FIG. 2 . For the relevant content of the clustering processing module 130 , please refer to the foregoing description of the step S130 .

The category determination module 140 is configured to determine a target point category in the at least one point category based on the dimension information of the at least one piece of target charging data in at least one of the dimensions. In this embodiment, the category determination module 140 may be configured to execute step S140 shown in FIG. 2 , and for the relevant content of the category determination module 140 , reference may be made to the foregoing description of step S140 .

The deviation value calculation module 150 is configured to, for each of the average charging voltages, calculate between the two target charging voltages corresponding to the average charging voltage at the charging moment based on the capacity increment curve corresponding to the target integral category. The target capacity deviation value of , wherein the capacity increment curve is formed based on the current time integral corresponding to each charging average voltage included in the target integral category. In this embodiment, the deviation value calculation module 150 may be configured to execute step S150 shown in FIG. 2 , and for the relevant content of the deviation value calculation module 150 , reference may be made to the foregoing description of step S150 .

The state determination module 160 is configured to determine the state information of the power supply device based on the target capacity deviation value. In this embodiment, the state determination module 160 may be configured to execute step S160 shown in FIG. 2 , and for the relevant content of the state determination module 160 , reference may be made to the foregoing description of step S160 .

In the embodiment of the present application, corresponding to the above-mentioned battery state monitoring method, a computer-readable storage medium is also provided, where a computer program is stored in the computer-readable storage medium, and the computer program executes the above-mentioned battery state monitoring method when running. of the various steps.

The steps performed when the aforementioned computer program is running will not be repeated here, and reference may be made to the foregoing explanation of the battery state monitoring method.

To sum up, the battery state monitoring method and device, electronic device and storage medium provided by the present application can firstly obtain the current (charging current) time integral in at least one dimension based on historical charging data, and secondly, based on each The current time integrals are clustered by dimension, and then capacity increment curves can be formed based on different types of current time integrals, so that the target capacity deviation value can be determined based on the capacity increment curve of the corresponding type of the target charging data, so that the target capacity deviation value can be determined based on the target capacity deviation value. The value determines the status information of the powered device. Based on this, the state of the power supply equipment can be effectively determined, so as to improve the problem that it is difficult to effectively monitor the battery state of the power supply equipment in the prior art, reduce the probability of a safety accident during the operation of the power supply equipment, and ensure that The safe operation of the power supply equipment itself and the application environment.

In the several embodiments provided by the embodiments of this application, it should be understood that the disclosed apparatus and method may also be implemented in other manners. The apparatus and method embodiments described above are only illustrative, for example, the flowcharts and block diagrams in the accompanying drawings show the architecture, possible implementation of the apparatus, method and computer program product according to various embodiments of the present application, function and operation. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more functions for implementing the specified logical function(s) executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions.

In addition, each functional module in each embodiment of the present application may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution, and the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, removable hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes . It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims (10)

1. A battery state monitoring method for monitoring a state of a power supply apparatus, wherein the power supply apparatus includes a plurality of battery cells, the method comprising:

acquiring at least one piece of historical charging data formed by historical charging of the power supply equipment and at least one piece of target charging data formed by current charging, wherein each piece of historical charging data comprises historical charging voltage of the plurality of single batteries at the same charging moment, and each piece of target charging data comprises target charging voltage of the plurality of single batteries at the same charging moment;

for each piece of historical charging data, acquiring current time integral of charging average voltage corresponding to the historical charging data on each preset dimension, wherein the charging average voltage is obtained based on a plurality of historical charging voltages included in the corresponding historical charging data, and the dimension is at least one of a temperature value dimension, a current value dimension, a difference dimension of moments before and after current change and an accumulated charging electric quantity value dimension;

in a feature space formed based on at least one dimension, clustering the obtained current time integrals to obtain at least one integral category, wherein any one integral category comprises one current time integral corresponding to each charging average voltage;

determining a target point category in the at least one point category based on dimension information of the at least one target charging data in at least one dimension;

for each charging average voltage, calculating a target capacity deviation value between two target charging voltages corresponding to the charging average voltage at the charging time based on a capacity increment curve corresponding to the target integration category, wherein the capacity increment curve is formed based on the current time integration corresponding to each charging average voltage included in the target integration category;

determining status information of the power supply apparatus based on the target capacity deviation value.

2. The method for monitoring the state of the battery according to claim 1, wherein the step of obtaining, for each piece of the historical charging data, a current-time integral of a charging average voltage corresponding to the historical charging data in each preset dimension includes:

for each piece of historical charging data, performing average value calculation processing on a plurality of historical charging voltages included in the historical charging data to obtain corresponding average charging voltage;

for each charging average voltage, determining a voltage range based on the charging average voltage, wherein each charging average voltage belongs to a corresponding voltage range, and any two voltage ranges do not overlap when a plurality of voltage ranges exist;

and calculating the capacity variation between the upper voltage limit value and the lower voltage limit value in each predetermined dimension of each voltage range, and taking the capacity variation as the current-time integral of the charging average voltage corresponding to the voltage range.

3. The battery state monitoring method according to claim 2, wherein the step of performing, for each piece of the historical charging data, an average calculation process based on a plurality of historical charging voltages included in the historical charging data to obtain a corresponding average charging voltage includes:

for each piece of historical charging data, carrying out probability distribution statistical processing on a plurality of historical charging voltages included in the historical charging data to obtain corresponding probability distribution information;

for each piece of probability distribution information, screening out at least one historical charging voltage with positive and negative deviation within a standard deviation range of a preset multiple in a plurality of corresponding historical charging voltages based on the probability distribution information;

and calculating the average value of the at least one historical charging voltage corresponding to the probability distribution information aiming at each piece of probability distribution information, and taking the average value as the charging average voltage of the historical charging data corresponding to the probability distribution information.

4. The battery state monitoring method according to claim 2, wherein the step of calculating, for each of the voltage ranges, a capacity change amount between an upper voltage limit value and a lower voltage limit value in the voltage range in each of predetermined dimensions includes:

determining at least one dimension from the dimension of the temperature value, the dimension of the current value, the dimension of the difference value of the current before and after the change of the current and the dimension of the accumulated charging electric quantity value;

for each voltage range, calculating the capacity variation between the upper voltage limit and the lower voltage limit in the voltage range in each dimension of the at least one dimension.

5. The battery state monitoring method according to any one of claims 1 to 4, wherein the step of calculating, for each of the charge average voltages, a target capacity deviation value between two target charge voltages corresponding to a charge time based on the capacity increment curve corresponding to the target integration class includes:

for each charging average voltage, determining a maximum target charging voltage and a minimum target charging voltage in a plurality of target charging voltages at a moment corresponding to the charging average voltage;

and calculating a target capacity deviation value between the maximum target charging voltage and the minimum target charging voltage corresponding to each charging average voltage based on the capacity increment curve corresponding to the target integration category.

6. The battery state monitoring method according to any one of claims 1 to 4, wherein the step of determining the state information of the power supply device based on the target capacity deviation value includes:

calculating a capacity deviation rate based on the target capacity deviation value and a historical capacity deviation value corresponding to the historical charging data;

and determining the state information of the power supply equipment based on the target capacity deviation value and the capacity deviation rate, wherein the state information comprises whether the power supply equipment belongs to an abnormal state or not.

7. The battery state monitoring method according to claim 6, wherein the step of determining the state information of the power supply apparatus based on the target capacity deviation value and the capacity deviation rate comprises:

acquiring a first weight coefficient and a second weight coefficient which are predetermined, wherein the first weight coefficient is generated based on the configuration of the target capacity deviation value, and the second weight coefficient is generated based on the configuration of the capacity deviation rate;

determining status information of the power supply apparatus based on the first weight coefficient, the second weight coefficient, the target capacity deviation value, the capacity deviation rate, and a predetermined determination threshold.

8. A battery state monitoring apparatus for monitoring a state of a power supply device including a plurality of unit batteries, the apparatus comprising:

the data acquisition module is used for acquiring at least one piece of historical charging data formed by historical charging of the power supply equipment and at least one piece of target charging data formed by current charging, wherein each piece of historical charging data comprises historical charging voltage of the plurality of single batteries at the same charging moment, and each piece of target charging data comprises target charging voltage of the plurality of single batteries at the same charging moment;

the integral acquisition module is used for acquiring current time integral of charging average voltage corresponding to the historical charging data on each preset dimension aiming at each piece of historical charging data, wherein the charging average voltage is obtained based on a plurality of historical charging voltages included by the corresponding historical charging data, and the dimension is at least one of a temperature value dimension, a current value dimension, a difference value dimension of moments before and after current change and an accumulated charging electric quantity value dimension;

the clustering processing module is used for clustering the obtained current time integrals in a feature space formed based on at least one dimension to obtain at least one integral category, wherein any one integral category comprises one current time integral corresponding to each charging average voltage;

a category determination module for determining a target point category among the at least one point category based on dimension information of the at least one target charging data in at least one of the dimensions;

a deviation value calculating module, configured to calculate, for each charging average voltage, a target capacity deviation value between two target charging voltages corresponding to the charging average voltage at a charging time based on a capacity increment curve corresponding to the target integration category, where the capacity increment curve is formed based on a current time integral corresponding to each charging average voltage included in the target integration category;

and the state determining module is used for determining the state information of the power supply equipment based on the target capacity deviation value.

9. An electronic device, comprising:

a memory for storing a computer program;

a processor coupled to the memory for executing a computer program stored in the memory to implement the battery condition monitoring method of any of claims 1-7.

10. A computer-readable storage medium storing a computer program, wherein the computer program is executed to implement the battery state monitoring method according to any one of claims 1 to 7.

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