CN112530049A - Battery state monitoring method and device, electronic equipment and storage medium - Google Patents
Battery state monitoring method and device, electronic equipment and storage medium Download PDFInfo
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0841—Registering performance data
Abstract
The application provides a battery state monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of battery state monitoring. In the method, current time integrals on at least one dimension are obtained based on historical charging data, then the current time integrals are clustered based on each dimension, and then capacity increment curves are respectively formed based on the current time integrals of different classes, so that a target capacity deviation value can be determined based on the capacity increment curves of the type corresponding to the target charging data, and the state information of the power supply equipment is determined based on the target capacity deviation value. Based on the method, the problem that the battery state of the power supply equipment is difficult to effectively monitor in the prior art can be solved.
Description
Technical Field
The present disclosure relates to the field of battery state monitoring technologies, and in particular, to a battery state monitoring method and apparatus, an electronic device, and a storage medium.
Background
The power supply equipment formed based on the chargeable and dischargeable battery is widely applied to the fields of electric automobiles, energy storage and the like, so that the safe operation of the power supply equipment becomes an important link in the use process, namely how to timely and accurately discover the abnormal state of the battery through monitoring data is an important link for large-scale safe operation of a battery system.
The existing battery abnormity identification scheme is mainly carried out in a driving mode based on a battery equivalent circuit model and a battery electrochemical mechanism model.
However, both of the above-described methods rely on high-precision and high-frequency data acquisition, and the amount of calculation is large. In large-scale data application, for example, when monitoring is performed on a current million-level operation battery system, calculation cannot be performed, and in a current monitoring platform, a single sampling precision or a sampling frequency cannot be applied to actual engineering based on an equivalent circuit model and an electrochemical mechanism model, so that the problem that effective monitoring of the battery state of a power supply device is difficult exists.
Disclosure of Invention
In view of the above, an object of the present application is to provide a battery status monitoring method and apparatus, an electronic device, and a storage medium, so as to solve the problem in the prior art that it is difficult to effectively monitor the battery status of a power supply device.
In order to achieve the above purpose, the embodiment of the present application adopts the following technical solutions:
a battery state monitoring method for monitoring a state of a power supply apparatus, wherein the power supply apparatus includes a plurality of unit batteries, 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 a charging average voltage corresponding to the historical charging data in 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;
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.
In a preferable selection of the embodiment of the present application, in the method for monitoring a battery state, 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.
In a preferred option of the embodiment of the present application, in the battery state monitoring method, 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.
In a preferable selection of the embodiment of the present application, in the battery state monitoring method, the step of calculating, for each of the voltage ranges, a capacity change amount between an upper voltage limit and a lower voltage limit 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.
In a preferred choice of this embodiment of the present invention, in the above battery state monitoring method, the step of calculating, for each of the charging average voltages, a target capacity deviation value between two corresponding target charging voltages at a charging time of the charging average voltage based on a capacity increment curve corresponding to the target integration type 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.
In a preferable selection of the embodiment of the present application, in the 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 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.
In a preferable selection of the embodiment of the present application, in the 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:
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.
The embodiment of the present application further provides a battery state monitoring device for monitoring the state of a power supply device, wherein the power supply device includes a plurality of battery cells, and the device includes:
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 each piece of historical charging data in 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;
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.
On the basis, an embodiment of the present application further provides an electronic device, including:
a memory for storing a computer program;
and the processor is connected with the memory and is used for executing the computer program stored in the memory so as to realize the battery state monitoring method.
On the basis of the foregoing, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed, the method for monitoring the battery state is implemented.
According to the battery state monitoring method and device, the electronic device and the storage medium, firstly, current (charging current) time integrals on at least one dimension can be obtained based on historical charging data, secondly, the current time integrals can be clustered based on each dimension, and then capacity increment curves can be respectively formed based on different types of current time integrals, so that a target capacity deviation value can be determined based on the capacity increment curves of the type corresponding to the target charging data, and the state information of the power supply device can be determined based on the target capacity deviation value. Based on this, can confirm power supply unit's state effectively to improve the problem that exists among the prior art and be difficult to carry out effective monitoring to power supply unit's battery state, reduce power supply unit and take place the probability of incident in the operation process, can guarantee power supply unit itself and the safe operation of applied environment, make to have higher practical value.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Fig. 2 is a schematic flow chart of a battery state monitoring method according to an embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating sub-steps included in step S120 in fig. 2.
Fig. 4 is a flowchart illustrating the sub-steps included in step S121 in fig. 3.
Fig. 5 is a flowchart illustrating the sub-steps included in step S123 in fig. 3.
Fig. 6 is a flowchart illustrating sub-steps included in step S150 in fig. 2.
Fig. 7 is a schematic diagram of a capacity increment curve provided in an embodiment of the present application.
Fig. 8 is a flowchart illustrating the sub-steps included in step S160 in fig. 2.
Fig. 9 is a flowchart illustrating the sub-steps included in step S162 in fig. 8.
Fig. 10 is a schematic block diagram of a battery state monitoring device according to an embodiment of the present disclosure.
Icon: 10-an electronic device; 12-a memory; 14-a processor; 100-battery state monitoring means; 110-a data acquisition module; 120-integral acquisition module; 130-a cluster processing module; 140-category determination module; 150-deviation value calculation module; 160-state determination module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1, an electronic device 10 according to an embodiment of the present disclosure may include a memory 12, a processor 14, and a battery status monitoring apparatus 100.
Wherein the memory 12 and the processor 14 are electrically connected directly or indirectly to realize data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The battery state monitoring device 100 includes at least one software functional module that can be stored in the memory 12 in the form of software or firmware (firmware). The processor 14 is configured to execute executable computer programs stored in the memory 12, for example, software functional modules and computer programs included in the battery state monitoring apparatus 100, so as to implement a battery state monitoring method (described below) provided by an embodiment of the present application.
Alternatively, the Memory 12 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 14 may be a general-purpose processor including a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and the like.
It will be appreciated that the configuration shown in FIG. 1 is merely illustrative and that the electronic device 10 may include more or fewer components than shown in FIG. 1 or may have a different configuration than shown in FIG. 1. For example, the electronic device 10 may further 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 status monitoring method applicable to the electronic device 10, for monitoring the status of a power supply device. The method steps defined by the flow related to the battery state monitoring method may be implemented by the electronic device 10.
The specific process shown in FIG. 2 will be described in detail below.
Step S110, acquiring at least one historical charging data of the power supply device that has been charged historically and at least one target charging data of the power supply device that is currently charged.
In this embodiment, the electronic device 10 may obtain at least one piece of historical charging data that is formed by charging the power supply device historically, and obtain at least one piece of target charging data that is formed by charging the power supply device currently.
The power supply device comprises a plurality of single batteries, each piece of historical charging data comprises historical charging voltages of the single batteries at the same charging time (so that a plurality of historical charging voltages can be obtained at one charging time), and each piece of target charging data comprises target charging voltages of the single batteries at the same charging time (so that a plurality of target charging voltages can be obtained at one charging time).
Step S120, for each piece of the historical charging data, obtaining a current time integral of the charging average 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 obtain, for each piece of historical charging data, a current-time integral of a charging average voltage corresponding to the historical charging data in each preset dimension.
Wherein the charging average voltage is obtained based on a plurality of historical charging voltages (i.e. historical charging voltages of a plurality of single batteries) included in the corresponding historical charging data, and the dimension is at least one.
Step S130, clustering the obtained current time integrals in a feature space formed based on at least one dimension to obtain at least one integral category.
In this embodiment, after obtaining the current time integral of the charge average voltage corresponding to each piece of historical charging data in each dimension based on step S120, the electronic device 10 may perform clustering processing on the obtained current time integral in a feature space formed based on the at least one dimension, so as to obtain at least one integral category.
Any one of the integration categories may include a current time integral corresponding to each charging average voltage.
Step S140, 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.
In this embodiment, after obtaining the at least one target charging data based on step S110 and obtaining the at least one integration category based on step S130, the electronic device 10 may determine the target integration category in the at least one integration category based on the dimension information in the at least one dimension included in the at least one target charging data.
Step S150, for each charging average voltage, based on the capacity increment curve corresponding to the target integral category, calculating a target capacity deviation value between two target charging voltages corresponding to the charging average voltage at the charging time.
In this embodiment, after determining the target integration category based on step S140, the electronic device 10 may calculate, for each of the charging average voltages, a target capacity deviation value between two target charging voltages corresponding to the charging time (i.e., the charging time of the charging average voltage is the same as the charging time of the two target charging voltages, wherein at least one charging time may be formed for each charging) based on the capacity increment curve corresponding to the target integration category.
Wherein the capacity increment curve may be formed based on a current-time integral corresponding to each charging average voltage included in the target integration category.
Step S160, determining the status information of the power supply device 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 status information of the power supply device based on the target capacity deviation value.
Based on the method, the state information of the power supply equipment can be effectively determined, so that the problem that the battery state of the power supply equipment is difficult to effectively monitor in the prior art is solved, and the probability of safety accidents of the power supply equipment in the operation process is reduced.
In the first aspect, it should be noted that, in step S110, a specific manner of obtaining the at least one piece of historical charging data is not limited, and may be selected according to an actual application requirement.
For example, in an alternative example, data obtained by collecting data (e.g., by a corresponding sensor) of the power supply device may be directly used as the historical charging data, wherein different historical charging data may be obtained based on different charging times.
For another example, in another alternative example, data obtained by data collection (e.g., collection by a corresponding sensor) of the power supply device may be first subjected to data cleaning, e.g., setting a corresponding threshold, so as to delete data exceeding the threshold, thereby obtaining the historical charging data.
For another example, in another alternative example, the data obtained by data acquisition (e.g., acquisition by a corresponding sensor) performed on the power supply device may be subjected to interpolation processing, such as nearest neighbor interpolation, linear interpolation, spline function interpolation, or piecewise multi-interpolation, in consideration of a problem that the data may be lost during transmission or storage, so as to obtain the historical charging data.
In another alternative example, the historical charging data may be obtained by performing filtering processing, such as mean filtering, median filtering, or wavelet filtering, on the data obtained by collecting the data (such as collecting the data through a corresponding sensor) of the power supply device, in consideration of a problem that the data may deviate from a true value due to interference of the device or due to a sampling frequency itself during the collection process.
In the second aspect, it should be noted that, in step S120, a specific manner of obtaining the current time integral is not limited, and may be selected according to a practical application requirement.
For example, in an alternative example, in conjunction with fig. 3, step S120 may include step S121, step S122, and step S123, which are described in detail below.
Step S121, for each piece of the historical charging data, performing 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.
In this embodiment, after the at least one piece of historical charging data is acquired based on step S110, for each piece of historical charging data, an average value calculation process may be performed based on a plurality of historical charging voltages included in the historical charging data, so that a corresponding average charging voltage may be obtained.
That is, one charging time may form one piece of historical charging data, and the historical charging data may include the historical charging voltage of each unit cell at the charging time, that is, there may be a plurality of historical charging voltages for a plurality of unit cells.
Step S122, for each of the charging average voltages, determining a voltage range based on the charging average voltage.
In this embodiment, after obtaining at least one charging average voltage based on step S121, a voltage range may be determined based on the charging average voltage (for example, if a plurality of charging average voltages are obtained, the voltage range may be divided by the plurality of charging average voltages).
Wherein each of the charging average voltages belongs to a corresponding voltage range, and any two voltage ranges do not overlap when multiple voltage ranges exist.
Step S123, for each of the voltage ranges, calculating a capacity variation between the upper voltage limit and the lower voltage limit in the voltage range in each predetermined dimension, and taking the capacity variation as a current-time integral of the charge average voltage corresponding to the voltage range.
In the present embodiment, after the voltage range of each of the charge average voltages is determined based on step S122, a capacity variation (e.g., a difference between a capacity corresponding to the voltage upper limit and a capacity corresponding to the voltage lower limit) between the voltage upper limit and the voltage lower limit in the voltage range is calculated in each dimension determined in advance, and the capacity variation is taken as a current time integral of the charge average voltage corresponding to the voltage range.
That is, if the average charging voltage is 3, the dimension is 3, and the time integral of the obtained current may be 3 × 3 — 27.
Alternatively, in the above example, the specific manner of performing the mean value calculation processing based on step S121 is not limited, and may be selected according to the actual application requirements.
For example, in an alternative example, in order to make the obtained charging average voltage better represent the overall information of the plurality of single batteries, in conjunction with fig. 4, step S121 may include step S121a, step S121b and step S121c, which are described in detail below.
Step S121a, for each piece of the historical charging data, performing 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, for each piece of historical charging data, probability distribution statistical processing may be performed on a plurality of historical charging voltages included in the historical charging data, so that corresponding probability distribution information may be obtained.
Step S121b is to screen out, for each of the probability distribution information, at least one of the historical charging voltages within a standard deviation range where the positive and negative deviations are a preset multiple, from the corresponding plurality of historical charging voltages, based on the probability distribution information.
In the present embodiment, after obtaining the probability distribution information based on step S121a, for each probability distribution information, at least one historical charging voltage within a standard deviation range with positive and negative deviations being a preset multiple (the preset multiple may be determined based on the calculation amount and the accuracy requirement) may be screened out from the corresponding plurality of historical charging voltages based on the probability distribution information, wherein the preset multiple may be 1 in an alternative example.
Step S121c is to calculate, for each of the probability distribution information, an average value of the at least one historical charging voltage corresponding to the probability distribution information, and use the average value as a charging average voltage of historical charging data corresponding to the probability distribution information.
In this embodiment, after filtering 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, an average value of the at least one historical charging voltage corresponding to the probability distribution information may be based on. Then, the average value may be used as the charging average voltage of the historical charging data corresponding to the probability distribution information.
Alternatively, in the above example, the specific way of calculating the capacity variation in each dimension based on step S123 is not limited, and may be selected according to the actual application requirement.
For example, in an alternative example, in order to improve the reliability of the obtained capacity change rate (i.e., current time integral), in conjunction with fig. 5, step S123 may include step S123a and step S123b, as described in detail below.
Step S123a, determine at least one dimension among a temperature value dimension, a current value dimension, a difference dimension between times before and after a current change, and an accumulated charging electric quantity value dimension.
In this embodiment, after determining the voltage range of each charging average voltage based on step S122, at least one dimension may be determined among a temperature value dimension, a current value dimension, a difference value dimension before and after the current change, and an accumulated charging electric quantity value dimension (where, based on different accuracy requirements, a different number of dimensions may be selected, for example, the higher the accuracy requirement, the more dimensions may be selected, so that the calculation is more fully dependent).
Step S123b, for each voltage range, calculating a capacity variation between the upper voltage limit and the lower voltage limit in the voltage range in each of the determined at least one dimension.
In the present embodiment, after determining at least one dimension based on step S123a, the capacity change amount between the upper voltage limit value and the lower voltage limit value in the voltage range may be calculated separately for each of the at least one dimension for each of the voltage ranges.
That is, if the average charging voltage is 3, the corresponding voltage range is 3, the dimension is 3, and the obtained capacity change amount may be 3 × 3 — 27.
In the third aspect, it should be noted that, in step S130, a specific manner of performing clustering processing on the obtained current time integrals is not limited, and may be selected according to actual application requirements.
For example, in an alternative example, the resulting at least one current time integral may be clustered at the feature control based on a K-MEANS algorithm, a K-MEDOIDS algorithm, or a CLARANS algorithm, and as such, at least one integral category may be obtained.
For another example, in another alternative example, the obtained at least one current time integral may be clustered based on a BIRCH algorithm, a CURE algorithm, or a chaleleon algorithm at the feature control, so that at least one integral category may be obtained.
For another example, in another alternative example, the obtained at least one current time integral may be clustered based on a DBSCAN algorithm, an OPTICS algorithm, or a cancel algorithm at the feature control, so that at least one integral category may be obtained.
In the fourth aspect, it should be noted that, in step S140, a specific manner for determining the target point category is not limited, and may be selected according to actual application requirements.
For example, in an alternative example, dimension information, such as temperature information, current information, and the like, of the at least one target charging data in each dimension may be obtained, and then, based on the dimension information, an objective point category may be determined in the at least one point category through a K-Nearest Neighbor (KNN) classification algorithm.
In the fifth aspect, it should be noted that, in step S150, a specific manner for calculating the target capacity deviation value is not limited, and may 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 unit cells, in conjunction with fig. 6, step S150 may include step S151 and step S152, which are described in detail below.
In step S151, for each of the charging average voltages, a maximum target charging voltage and a minimum target charging voltage are determined from a plurality of target charging voltages at a time corresponding to the charging average voltage.
In this embodiment, after obtaining the charging average voltage corresponding to each piece of the historical charging data, a maximum target charging voltage and a minimum target charging voltage (two target charging voltages with the largest positive and negative deviations) may be determined from a plurality of target charging voltages at a time corresponding to the charging average voltage.
That is, if the charging average voltage is 3.5V, for the at least one piece of target charging data, the voltage average value of the target charging voltages corresponding to each piece of target charging data may be determined, then the target charging data with the voltage average value of 3.5V is determined, and then the maximum target charging voltage and the minimum target charging voltage are determined from the target charging voltages corresponding to the target charging data. As such, for each of the charging average voltages, a corresponding maximum target charging voltage and a corresponding minimum target charging voltage may be obtained.
Step S152 is to calculate, for each charging average voltage, a target capacity deviation value between a maximum target charging voltage and a minimum target charging voltage corresponding to the charging average voltage based on the capacity increment curve corresponding to the target integration type.
In this embodiment, after determining the maximum target charging voltage and the minimum target charging voltage corresponding to each charging average voltage based on step S151, the target capacity deviation value between the maximum target charging voltage and the minimum target charging voltage corresponding to each charging average voltage may be calculated based on the capacity increment curve corresponding to the target integration class obtained in step S140 (the capacity increment curve is formed by current time integration included in the target integration class, as shown in fig. 7).
For example, in the example shown in fig. 7, when the maximum target charging voltage is 3.8V and the minimum target charging voltage is 3.6V, an area enclosed by the capacity increase curve, the abscissa axis, a straight line whose abscissa is 3.6V, and a straight line whose abscissa is 3.8V is calculated, and the area is set as the target capacity deviation value.
In the sixth aspect, it should be noted that, in step S160, a specific manner for determining the status information of the power supply device is not limited, and may be selected according to actual application requirements.
For example, in an alternative example, in order to improve the accuracy of the determined state information, in conjunction with fig. 8, step S160 may include step S161 and step S162, as described below.
Step S161 calculates a capacity deviation rate based on the target capacity deviation value and a history capacity deviation value corresponding to the history charging data.
In this embodiment, after obtaining the target capacity deviation value based on step S150, a historical capacity deviation value corresponding to the historical charging data may also be obtained (there may be a plurality of historical capacity deviation values corresponding to different historical stages, such as the first charging, the second charging, the third charging, the fourth charging, etc.), and thus, a capacity deviation rate (i.e., a capacity deviation change rate) may be calculated based on the target capacity deviation value and the historical capacity deviation value.
And step S162, determining the state information of the power supply equipment based on the target capacity deviation value and the capacity deviation rate.
In this embodiment, after obtaining the capacity deviation rate based on step S161, the status information of the power supply device may be determined together with the target capacity deviation value in conjunction with the capacity deviation surcharge (the status information may be made more reliable due to the basis or increase in dimension for determination).
Wherein the state information includes whether the power supply apparatus belongs to an abnormal state.
It is to be understood that, when the target capacity deviation value is plural, that is, the target capacity deviation values corresponding to plural charging times, the capacity deviation rate corresponding to the target capacity deviation value corresponding to the charging time and the historical capacity deviation value may be calculated for each charging time. In this way, the state information of the power supply apparatus can be determined more reliably by the target capacity deviation values and the capacity deviation rates corresponding to a plurality of charging timings.
Optionally, the specific manner of determining the status 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, in conjunction with fig. 9, step S162 may include step S162a and step S162b, as described in detail below.
In step S162a, a first weighting factor and a second weighting factor determined in advance are acquired.
In this embodiment, after obtaining the capacity deviation rate based on step S161, a first weighting factor and a second weighting factor, which are configured and generated in advance for the capacity deviation rate and the target capacity deviation value, respectively, may be obtained.
The first weight coefficient is generated based on configuration of the target capacity deviation value, and the second weight coefficient is generated based on configuration of the capacity deviation rate (wherein, when the target capacity deviation value is weighted more according to actual demand, the first weight coefficient can be larger than the second weight coefficient, and when the capacity deviation rate is weighted more according to actual demand, the first weight coefficient can be smaller than the second weight coefficient).
Step S162b of determining the 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.
In this embodiment, after the first weight coefficient and the second weight coefficient are obtained based on step S162a, the target capacity deviation value and the capacity deviation rate may be weighted and calculated based on the first weight coefficient and the second weight coefficient (considering that the target capacity deviation value and the capacity deviation rate belong to parameters with different dimensions, normalization processing and weighting calculation may be performed respectively), and then, based on the obtained weighted values and a predetermined determination threshold, status information of the power supply device may be determined (for example, if the weighted values are greater than the determination threshold, the status information may be that the power supply device belongs to an abnormal state).
It should be noted that, when step S160 is executed, if it is necessary to determine the state information of the power supply device based on the variation trend of the target capacity deviation value, a historical target capacity deviation value that has been historically charged a plurality of times may be acquired. In this way, a time-based capacity deviation value variation trend can be obtained, or when the power supply device is applied to an electric automobile, a capacity deviation value variation trend based on the driving mileage can also be obtained. Further, in order to improve the reliability of determining the state information of the power supply apparatus based on the variation tendency, interpolation processing of the capacity deviation value (an interpolation method such as linear interpolation) may be performed on the variation tendency of the capacity deviation value based on the time or the mileage.
With reference to fig. 10, the present embodiment further provides a battery status monitoring apparatus 100 applicable to the electronic device 10. The battery state monitoring apparatus 100 may include a data acquisition module 110, an integral acquisition module 120, a cluster processing module 130, a category determination module 140, a deviation value calculation module 150, and a state determination module 160.
The data obtaining module 110 is configured to obtain at least one piece of historical charging data formed by charging the power supply device historically and at least one piece of target charging data formed by charging the power supply device currently, where each piece of historical charging data includes historical charging voltages of the plurality of single batteries at the same charging time, and each piece of target charging data includes target charging voltages of the plurality of single batteries at the same charging time. In this embodiment, the data obtaining module 110 may be configured to execute step S110 shown in fig. 2, and reference may be made to the foregoing description of step S110 for relevant contents of the data obtaining module 110.
The integral obtaining module 120 is configured to obtain, 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, where 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. In this embodiment, the integral obtaining module 120 may be configured to perform step S120 shown in fig. 2, and reference may be made to the foregoing description of step S120 for relevant contents of the integral obtaining module 120.
The clustering module 130 is configured to perform clustering on the obtained current time integrals in a feature space formed based on at least one of the dimensions to obtain at least one integral category, where any one integral category includes one current time integral corresponding to each charging average voltage. In this embodiment, the clustering module 130 can be configured to perform step S130 shown in fig. 2, and reference may be made to the foregoing description of step S130 for relevant contents of the clustering module 130.
The category determining module 140 is configured to determine 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 dimension. In this embodiment, the category determining module 140 may be configured to execute step S140 shown in fig. 2, and reference may be made to the foregoing description of step S140 regarding the relevant content of the category determining module 140.
The deviation value calculating module 150 is configured to calculate, for each charging average voltage, a target capacity deviation value between two target charging voltages corresponding to the charging time of the charging average voltage 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. In this embodiment, the deviation value calculating module 150 may be configured to execute step S150 shown in fig. 2, and reference may be made to the foregoing description of step S150 for relevant contents of the deviation value calculating module 150.
The status determining module 160 is configured to determine status information of the power supply device based on the target capacity deviation value. In this embodiment, the status determining module 160 may be configured to execute step S160 shown in fig. 2, and reference may be made to the foregoing description of step S160 for relevant contents of the status determining module 160.
In an embodiment of the present application, a computer-readable storage medium is provided, where a computer program is stored, and the computer program executes the steps of the battery state monitoring method when running.
The steps executed when the computer program runs are not described in detail herein, and reference may be made to the explanation of the battery state monitoring method above.
To sum up, according to the battery state monitoring method and apparatus, the electronic device, and the storage medium provided by the present application, first, a current (charging current) time integral in at least one dimension may be obtained based on historical charging data, then, the current time integral may be clustered based on each dimension, and then, capacity increment curves may be respectively formed based on different types of current time integrals, so that a target capacity deviation value may be determined based on a capacity increment curve of a type corresponding to target charging data, and thus, state information of the power supply device may be determined based on the target capacity deviation value. Based on this, can confirm the state of power supply unit effectively to improve the problem that exists among the prior art and be difficult to effectively monitor the battery state of power supply unit, reduce the probability that power supply unit takes place the incident in the operation process, can guarantee the safe operation of power supply unit itself and application environment.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). 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 shown 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 will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present 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 a charging average voltage corresponding to the historical charging data in 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;
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 battery state monitoring method 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 condition monitoring method according to claim 6, wherein the step of determining the condition information of the power supply apparatus based on the target capacity deviation value and the capacity deviation rate includes:
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 each piece of historical charging data in 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;
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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