CN110927606B

CN110927606B - Battery state monitoring method and device

Info

Publication number: CN110927606B
Application number: CN201911071955.5A
Authority: CN
Inventors: 夏恒; 贾庆山; 唐静娴; 赵千川; 夏俐; 管晓宏; 高江; 岳上; 韩建军; 黄现东; 栗权; 林森; 赵静洲; 徐东; 郑焕琼
Original assignee: Tsinghua University; Tencent Technology Shenzhen Co Ltd
Current assignee: Tsinghua University; Tencent Technology Shenzhen Co Ltd
Priority date: 2019-11-05
Filing date: 2019-11-05
Publication date: 2022-01-25
Anticipated expiration: 2039-11-05
Also published as: CN110927606A

Abstract

The embodiment of the application provides a battery state monitoring method and device, a computer readable medium and electronic equipment. The monitoring method comprises the following steps: acquiring a first characteristic parameter of a battery; determining a second characteristic parameter of the battery according to the first characteristic parameter of the battery; inputting the first characteristic parameter and/or the second characteristic parameter of the battery into a battery state monitoring model trained in advance, and outputting a battery state parameter; monitoring a state of the battery based on the battery state parameter. The technical scheme of the embodiment of the application can enhance the transportability of monitoring the battery state under different conditions.

Description

Battery state monitoring method and device

Technical Field

The application relates to the technical field of power supply monitoring, in particular to a battery state monitoring method and device.

Background

In a battery state monitoring scenario, for example, in a monitoring scenario of a health condition of a UPS battery, a specially-assigned person usually performs a discharge test on the battery at regular intervals, detects a battery capacity, and uses a ratio of an actual capacity to a rated capacity as a representation of the health condition of the battery, or automatically monitors the battery state on line, that is, a corresponding mathematical model or an intelligent algorithm is established based on a large amount of experimental (especially charge-discharge experimental) data, and the battery health condition is evaluated by collecting the battery data through an automatic system such as a battery polling instrument. However, how to enhance the portability of monitoring the battery state under different conditions is an urgent technical problem to be solved.

Disclosure of Invention

Embodiments of the present application provide a battery state monitoring method, an apparatus, a computer-readable medium, and an electronic device, which can enhance portability of monitoring a battery state under different conditions to at least a certain extent.

Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.

According to an aspect of an embodiment of the present application, there is provided a battery state monitoring method including: acquiring a first characteristic parameter of a battery; determining a second characteristic parameter of the battery according to the first characteristic parameter of the battery; inputting the first characteristic parameter and/or the second characteristic parameter of the battery into a battery state monitoring model trained in advance, and outputting a battery state parameter; monitoring a state of the battery based on the battery state parameter.

According to an aspect of an embodiment of the present application, there is provided a battery state monitoring apparatus including: an acquisition unit configured to acquire a first characteristic parameter of a battery; a determining unit, configured to determine a second characteristic parameter of the battery according to the first characteristic parameter of the battery; the output unit is used for inputting the first characteristic parameter and/or the second characteristic parameter of the battery into a battery state monitoring model trained in advance so as to output a battery state parameter; a monitoring unit for monitoring the state of the battery based on the battery state parameter.

In some embodiments of the present application, based on the foregoing scheme, the obtaining unit is configured to: acquiring first characteristic parameters of the battery at historical moments and historical decline time intervals of the battery; the determination unit is configured to: determining second characteristic parameters of the battery at various historical moments according to the first characteristic parameters, and determining battery states of the battery at various historical moments according to decay time intervals of the battery at historical moments; the battery state monitoring device further includes: and the model training unit is used for training and generating the battery state monitoring model based on the first characteristic parameter and/or the second characteristic parameter of the battery at each historical moment and the battery state at each historical moment.

In some embodiments of the present application, based on the foregoing scheme, the obtaining unit is configured to: acquiring initial characteristic parameters of the battery at various historical moments; detecting whether the initial characteristic parameters are abnormal or not; and acquiring the initial characteristic parameter without the abnormality as a first characteristic parameter.

In some embodiments of the present application, based on the foregoing scheme, the obtaining unit is configured to: and acquiring the current value and/or the voltage value and/or the internal resistance value and/or the temperature value of the battery at various historical moments.

In some embodiments of the present application, based on the foregoing solution, the first characteristic parameter of the battery at each historical time includes a current value of the battery at each historical time, and the obtaining unit is configured to: before determining second characteristic parameters of the battery at historical moments according to the first characteristic parameters, detecting whether current values of the battery at historical moments are larger than a preset threshold value; and filtering a first characteristic parameter corresponding to the moment when the current value is greater than a preset threshold value.

In some embodiments of the application, based on the foregoing solution, the first characteristic parameter of the battery at each historical time includes a voltage value and/or an internal resistance value of the battery at each historical time, and the determining unit is configured to: and determining the relative voltage value and/or the relative internal resistance value and/or the voltage change value and/or the internal resistance change value and/or the voltage gradient value and/or the internal resistance gradient value and/or the voltage internal resistance ratio of the battery at each historical moment according to the voltage value and/or the internal resistance value of the battery at each historical moment.

In some embodiments of the present application, based on the foregoing scheme, the obtaining unit is configured to: determining a first moment when the battery meets a fault replacement condition and a second moment when the battery is replaced; determining a performance decay time experienced before the battery enters a fault state; and determining a historical degradation time interval of the battery according to the first time and the second time and the performance degradation time before the battery enters the fault state.

In some embodiments of the present application, based on the foregoing scheme, the determining unit is configured to: determining the battery state at each moment in the decline time interval as an early warning state; and determining the battery state at each moment in other time intervals except the decline time interval as the state of health.

In some embodiments of the present application, based on the foregoing scheme, the determining unit is configured to: determining sub-fading time intervals in the fading time interval, wherein the fading degrees of the battery in different sub-fading time intervals are different; and determining the early warning state grade of the battery in different sub-fading time intervals according to the fading degree of the battery in different sub-fading time intervals.

According to an aspect of embodiments of the present application, there is provided a computer-readable medium on which a computer program is stored, the computer program, when executed by a processor, implementing the battery state monitoring method as described in the above embodiments.

According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the battery condition monitoring method as described in the above embodiments.

In the technical solutions provided in some embodiments of the present application, a first characteristic parameter of a battery and a second characteristic parameter determined by the first characteristic parameter are obtained as input data, a battery state parameter is output through the pre-trained battery state monitoring model, and further, the state of the battery is monitored by the battery state parameter. According to the technical scheme, a fixed evaluation model is not required to be established in advance, and the battery state monitoring model is obtained through battery historical data training, so that the battery state monitoring model is not limited to a certain specific battery model or a certain use scene, and the portability of the technical scheme is high. In addition, the technical scheme of the application realizes the full-process automation of battery state monitoring, monitors and processes data on line, does not need to establish a fixed mathematical model manually, and greatly saves the labor cost input on site and in technology.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:

FIG. 1 shows a schematic diagram of an exemplary system architecture to which aspects of embodiments of the present application may be applied;

FIG. 2 shows a flow diagram of a battery condition monitoring method according to an embodiment of the present application;

FIG. 3 illustrates a detailed flow diagram for obtaining the battery state monitoring model according to one embodiment of the present application;

FIG. 4 shows a flow chart for obtaining first characteristic parameters of the battery at various historical times according to an embodiment of the application;

FIG. 5 shows a detailed flowchart for obtaining first characteristic parameters of the battery at various historical times according to one embodiment of the application;

FIG. 6 illustrates a flow diagram of a method prior to determining second characteristic parameters of the battery at historically various times according to one embodiment of the present application;

FIG. 7 illustrates a detailed flow diagram for obtaining historical decay time intervals for a battery according to one embodiment of the present application;

FIG. 8 illustrates a detailed flow diagram for determining the battery status of the battery at various times in the history according to one embodiment of the present application;

fig. 9 shows a detailed flowchart for determining the battery status at each time within the decay time interval as an early warning status according to an embodiment of the present application;

FIG. 10 shows a schematic diagram of a supervised learning approach according to one embodiment of the present application;

FIG. 11 shows a scene schematic of a battery condition monitoring method according to an embodiment of the present application;

FIG. 12 shows a block diagram of a battery condition monitoring device according to an embodiment of the present application;

FIG. 13 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

Fig. 1 shows a schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied.

As shown in fig. 1, the system architecture may include terminal devices (e.g., one or more of the smart phone 101, the tablet computer 102, and the portable computer 103 shown in fig. 1, and certainly a desktop computer, etc.) or apparatuses, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between terminal devices and the server 105. Network 104 may include various connection types, such as wired communication links, wireless communication links, and so forth.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, server 105 may be a server cluster comprised of multiple servers, or the like.

In one embodiment of the present application, the battery status may be monitored remotely. For example, it may be that the battery installed in the server 105 is remotely monitored by a terminal device such as the smartphone 101 shown in fig. 1. Specifically, the first characteristic data of the battery in the server 105 may be acquired by a data acquisition device in the server 105, the smartphone 101 acquires the first characteristic data through the network 104, and determines the second characteristic data according to the first characteristic data, and further, the first characteristic parameter and/or the second characteristic parameter of the battery are input to a battery state monitoring model arranged in the smartphone 101, and then battery state parameters capable of monitoring the battery state are output.

In one embodiment of the present application, the battery status may also be monitored locally. For example, it may be that a battery installed in the apparatus is locally monitored by a monitoring device provided in the apparatus, in which a battery state monitoring model is provided.

It should be noted that the battery state monitoring method provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the battery state monitoring device is generally disposed in the server 105. However, in other embodiments of the present application, the terminal device may also have a similar function as the server, so as to execute the battery status monitoring scheme provided by the embodiments of the present application.

The implementation details of the technical solution of the embodiment of the present application are set forth in detail below:

according to a first aspect of the present disclosure, a battery condition monitoring method is provided.

Referring to fig. 2, a flow diagram of a battery status monitoring method according to an embodiment of the present application is shown, which may be performed by a device having a computing processing function, such as the server 105 shown in fig. 1, or by a terminal device as shown in fig. 3. As shown in fig. 2, the battery state monitoring method at least includes steps 310 to 370:

in step 310, a first characteristic parameter of the battery is obtained.

Step 330, determining a second characteristic parameter of the battery according to the first characteristic parameter of the battery.

And 350, inputting the first characteristic parameter and/or the second characteristic parameter of the battery into a battery state monitoring model trained in advance, and outputting a battery state parameter.

Step 370, monitoring the state of the battery based on the battery state parameter.

The steps carried out as above will be explained in detail below:

in step 310, a first characteristic parameter of the battery is obtained.

It should be noted that the acquired first characteristic parameter of the battery is mainly used for monitoring the state of the battery in real time, and therefore, the acquired first characteristic parameter refers to the real-time first characteristic parameter.

In this application, the battery may include a single battery cell, where the single battery cell corresponds to one or a group of first characteristic parameters, and the group of first characteristic parameters includes a plurality of first characteristic parameters.

In the present application, the battery may also include a set of cells. It should be noted that a plurality of battery cells are included in a group of battery cells, and it should be understood that the plurality of battery cells should correspond to a plurality of or a plurality of groups of the first characteristic parameters.

In an embodiment of the present application, the obtaining of the first characteristic parameter of the battery may be implemented by:

firstly, collecting initial characteristic parameters of the battery through a collecting device. Then, whether the initial characteristic parameters have abnormity is detected. And finally, acquiring the initial characteristic parameters without the abnormality as first characteristic parameters.

In a specific implementation of an embodiment, the collecting the initial characteristic parameter of the battery may include at least one of:

firstly, collecting the real-time current value of the battery.

And secondly, collecting the real-time voltage value of the battery.

And thirdly, collecting the real-time internal resistance value of the battery.

And fourthly, acquiring a real-time temperature value of the battery.

In one embodiment of the present application, the battery may be a UPS battery, and the collected battery initial characteristic parameter refers to a current value and/or a voltage value and/or an internal resistance value and/or a temperature value when the UPS battery is in a float state.

It should be explained that the battery float charge refers to a way for the battery to operate electrically, i.e. the system connects the battery in parallel with the power line to the load circuit, the voltage of which is substantially constant and only slightly higher than the terminal voltage of the battery, and the loss of the local effect of the battery is compensated by the small current supplied by the power line, so that it can be always kept in a state of charge satisfaction without overcharging. Therefore, the battery can be charged and discharged along with the fluctuation of the voltage of the power line. When the load is light and the voltage of the power line is high, the battery is charged, and when the load is heavy or the power supply is interrupted unexpectedly, the battery is discharged to share part or all of the load. Thus, the battery has the function of voltage stabilization and is in a standby state.

In an embodiment of the present application, the abnormality of the initial characteristic parameter may refer to a case that the initial characteristic parameter is too large or too small, and may also refer to a case that the initial characteristic parameter is missing.

It should be noted that, in the present application, if the initial characteristic parameter of the battery refers to a set of initial characteristic parameters, that is, includes a plurality of initial characteristic parameters, when an initial characteristic parameter in the set of initial characteristic parameters is abnormal, the set of initial characteristic parameters is considered to be abnormal, and therefore, the set of initial characteristic parameters will not be acquired.

For example, the initial characteristic parameters of the battery include a current value, a voltage value, an internal resistance value, and a temperature value. If the current value is missing, the voltage value, the internal resistance value and the temperature value of the battery are not acquired as the first characteristic parameter even if the voltage value, the internal resistance value and the temperature value of the battery are normal.

With continued reference to fig. 2, in step 330, a second characteristic parameter of the battery is determined based on the first characteristic parameter of the battery.

In one embodiment of the present application, the first characteristic parameter of the battery comprises a current value, and before determining the second characteristic parameter of the battery according to the first characteristic parameter, the method further comprises: detecting whether the current value of the battery is larger than a preset threshold value; and filtering the corresponding first characteristic parameter of which the current value is greater than a preset threshold value.

Specifically, for example, the first characteristic parameter of the battery includes a current value, a voltage value, an internal resistance value, and a temperature value. If the current value is greater than a predetermined threshold value, a first characteristic parameter comprising the current value, the voltage value, the internal resistance value and the temperature value is filtered.

In an embodiment of the application, the first characteristic parameter of the battery may include at least one of a battery voltage value and a battery internal resistance value, and the determining the second characteristic parameter of the battery according to the first characteristic parameter includes at least one of the following manners:

firstly, the relative voltage value of the battery is determined according to the voltage value of the battery.

Secondly, determining the voltage change value of the battery according to the voltage value of the battery.

Thirdly, determining the voltage gradient value of the battery according to the voltage value of the battery.

Fourthly, determining the relative internal resistance value of the battery according to the internal resistance value of the battery.

Fifthly, determining the internal resistance change value of the battery according to the internal resistance value of the battery.

Sixthly, determining the internal resistance gradient value of the battery according to the internal resistance value of the battery.

Seventhly, determining the voltage internal resistance ratio of the battery according to the voltage value and the internal resistance value of the battery.

As described above, it is understood that the manner of determining the second characteristic parameter of the battery according to the first characteristic parameter may be arbitrary and is not limited to those shown above.

With continued reference to fig. 2, in step 350, the first characteristic parameter and/or the second characteristic parameter of the battery are input into a battery state monitoring model trained in advance, and the battery state parameter is output.

In one embodiment of the present application, the battery state monitoring model may be obtained by a method as shown in fig. 3.

Referring to fig. 3, a detailed flowchart of a method for obtaining the battery state monitoring model according to an embodiment of the present application is shown, which may specifically include steps 3530 to 3570:

step 3530, obtaining first characteristic parameters of the battery at historical time points, and determining second characteristic parameters of the battery at historical time points according to the first characteristic parameters.

In a specific implementation of an embodiment, the obtaining of the first characteristic parameter of the battery at each historical time may be implemented by steps shown in fig. 4.

Referring to fig. 4, a detailed flowchart for obtaining the battery state monitoring model according to an embodiment of the present application is shown, which may specifically include steps 3531 to 3533:

step 3531, collecting initial characteristic parameters of the battery at various historical moments.

It should be noted that the initial feature parameter may be one initial feature parameter or may be a set of initial feature parameters, where a set of initial feature parameters may include a plurality of initial feature parameters.

In the application, before the battery state monitoring model is trained, the initial characteristic parameters of the battery monomers can be collected in real time through a sensor, and data are stored. It will be appreciated by those skilled in the art that the initial characteristic parameter acquisition is performed for a sufficient period of time and is continued so that the initial characteristic parameter of the battery may be acquired at various times in the history. It should also be understood that the time interval between each adjacent time in the history may be any, for example, 1 second or 1 minute. If the time interval between each of the historical adjacent times is 1 second, then within 1 hour, 60 x 60 or 60 x 60 sets of the initial characteristic parameters of the cells should be collected.

In the present application, the battery may be one battery, i.e. one single battery, or may be a group of batteries, i.e. a plurality of single batteries. If the battery comprises a plurality of single batteries, at each moment, a plurality of or a plurality of groups of initial characteristic parameters should be collected. For example, the battery includes 10 single batteries, and the time interval between each adjacent time is 1 second historically, then within 1 hour, 10 × 60 × 60 or 10 × 60 × 60 sets of initial characteristic parameters of the battery should be acquired.

Specifically, the collecting of the initial characteristic parameters of the battery at each historical time may include at least one of:

firstly, collecting current values of the battery at various historical moments.

And secondly, collecting voltage values of the battery at various historical moments.

And thirdly, collecting the internal resistance value of the battery at each historical moment.

Fourthly, collecting temperature values of the battery at various historical moments.

Step 3532, detecting whether the initial characteristic parameters have abnormality.

Step 3533, the initial characteristic parameters without the abnormality are acquired as first characteristic parameters.

In a specific implementation of an embodiment, the collected initial characteristic parameters of the battery at various historical time points include a set of initial characteristic parameters including a current value, a voltage value, an internal resistance value, and a temperature value of the battery. The detecting whether the initial characteristic parameter has an abnormality or not, and acquiring the initial characteristic parameter without the abnormality as the first characteristic parameter may be implemented by a process as shown in fig. 5.

Referring to fig. 5, a detailed flowchart for acquiring the first characteristic parameter of the battery at each historical time point according to an embodiment of the present application is shown, and the detailed contents are as follows:

firstly, a group of initial characteristic parameters including a current value, a voltage value, an internal resistance value and a temperature value of the battery are input. And secondly, sequentially detecting whether the voltage value, the current value, the internal resistance value and the temperature value of the battery are abnormal or missing, discarding the group of initial characteristic parameters if one exists, and outputting the group of initial characteristic parameters as a first characteristic parameter if none exists.

In the present application, it is preferable that the data samples of the first characteristic parameter of the battery obtained may be sufficient.

The benefits realized in the above embodiments are: sensor readings are abnormal, as data collected in the field is usually not completely reliable. When a certain characteristic parameter (such as a voltage value) of the single battery at a certain moment exceeds a set threshold value, the value is judged to be abnormal. In addition, a certain packet loss rate also exists in the data transmission process, which results in data loss, that is, a value stored in a certain characteristic parameter (such as a voltage value) at a certain time is a null value. Therefore, by detecting the initial characteristic parameters to clean the data, the sample data for model training can be guaranteed to be reasonable.

In a specific implementation of an embodiment, the first characteristic parameter of the battery at each historical time includes a current value of the battery at each historical time, and before determining the second characteristic parameter of the battery at each historical time according to the first characteristic parameter, the method steps shown in fig. 6 may be further implemented.

Referring to fig. 6, a flowchart of a method before determining second characteristic parameters of the battery at various historical time points according to an embodiment of the present application is shown, which may specifically include steps 3511 to 3512:

step 3511, detecting whether the current value of the battery at each historical moment is larger than a preset threshold value.

Step 3512, filtering the first characteristic parameter corresponding to the moment when the current value is greater than the preset threshold value.

Specifically, the predetermined threshold of the current value may be set according to actual conditions, and may be set to 2A (amperes) or 3A (amperes), for example.

The benefits of the specific implementation in the above embodiments are: whether the current value of the battery at each historical moment is larger than a preset threshold value or not can be judged, and whether the battery corresponding to the moment is in a floating charge state or not can be judged. In the application, the first characteristic data of the battery in the floating charge state is acquired, so that a more applicable battery state monitoring model can be trained.

In a specific implementation of an embodiment, the first characteristic parameter of the battery at each historical time includes at least one of a voltage value of the battery at each historical time and an internal resistance value of the battery at each historical time, and the determining the second characteristic parameter of the battery at each historical time according to the first characteristic parameter includes at least one of the following manners:

firstly, the relative voltage value of the battery at each historical moment is determined according to the voltage value of the battery at each historical moment.

Specifically, the relative voltage value of the battery at each historical time may be obtained according to the following formula:

wherein, RV_i ^tA relative voltage value V representing the historical time t of the ith single battery in the battery (battery pack)_i ^tThe voltage value of the ith single battery in the battery (battery pack) at the historical time t is shown, and G shows the number of the single batteries in the battery (battery pack).

And secondly, determining the voltage change value of the battery at each historical time according to the voltage value of the battery at each historical time.

Specifically, the voltage variation value of the battery at each historical time can be obtained according to the following formula:

wherein,

representing the voltage value of the single battery at the historical time T relative to T-T_changeTo T-T_changeThe change value of the average voltage value in the interval time interval; v_i ^tRepresenting the voltage value of the ith single battery in the battery (battery pack) at the historical time t; interval is shown at T-T_changeTo T-T_changeThe number of times within the interval.

Thirdly, determining the voltage gradient value of the battery at each historical moment according to the voltage value of the battery at each historical moment.

Specifically, the voltage gradient value of the battery at each historical time can be obtained according to the following calculation process:

in the present application, it is assumed that

For indicating the voltage of the cells at different moments in time,

for indicating the time axis at different times, the voltage gradient

It means at T-T_gradSlope of the least squares linear fit of the cell voltages over the time interval to t.

Specifically, least squares linear fitting refers to finding two real numbers a₀And a₁Minimizing the following equation:

wherein, a₁I.e. the voltage gradient at time t

Fourthly, determining the relative internal resistance value of the battery at each historical moment according to the internal resistance value of the battery at each historical moment.

Specifically, the relative internal resistance value of the battery at each historical time can be obtained according to the following formula:

wherein,

a relative internal resistance value of the ith single battery in the battery (battery pack) at the historical time t,

The internal resistance value of the ith single battery in the battery (battery pack) at the historical time t is shown, and G shows the number of the single batteries in the battery (battery pack).

Fifthly, determining the internal resistance change value of the battery at each historical moment according to the internal resistance value of the battery at each historical moment.

Specifically, the internal resistance change value of the battery at each historical time may be obtained according to the following formula:

wherein,

representing the internal resistance value of the single battery at the historical time T relative to the T-T_changeTo T-T_changeThe change value of the average internal resistance value in the interval time interval;

representing the internal resistance value of the ith single battery in the battery (battery pack) at the historical time t; interval is shown at T-T_changeTo T-T_changeThe number of times within the interval.

Sixthly, determining the internal resistance gradient value of the battery at each historical moment according to the internal resistance value of the battery at each historical moment.

Specifically, the internal resistance gradient value of the battery at each historical time may be obtained according to the following calculation process:

in the present application, it is assumed that

Which is used for representing the internal resistance of the single battery at different moments,

for indicating the time axis at different times, the internal resistance gradient

It means at T-T_gradSlope of least squares linear fit of cell internal resistance over the time interval to t.

wherein, a₁I.e. internal resistance gradient at time t

Seventhly, determining the voltage internal resistance ratio of the battery at each historical moment according to the voltage value and the internal resistance value of the battery at each historical moment.

Specifically, the voltage internal resistance ratio refers to a ratio between a voltage value of the single battery and an internal resistance value of the single battery, that is:

as described above, it is understood that the manner of determining the second characteristic parameter of the battery at each time in the history may be arbitrary according to the first characteristic parameter, and is not limited to those shown above.

Step 3550, obtaining a historical decline time interval of the battery, and determining the battery state of the battery at each historical moment according to the historical decline time interval of the battery.

In a specific implementation of an embodiment, the obtaining of the historical decay time interval of the battery may be implemented by the steps shown in fig. 7.

Referring to fig. 7, a detailed flowchart for acquiring a historical degradation time interval of a battery according to an embodiment of the present application is shown, which may specifically include steps 3551 to 3553:

step 3551, determine a first time at which the battery meets a faulty replacement condition and a second time at which the battery is replaced.

Step 3552, determine the performance decay time experienced before the battery enters a fault state.

Step 3553 determines a historical degradation time interval of the battery based on the first time and the second time and a performance degradation time experienced before the battery enters a fault state.

In the present application, the battery typically experiences a cyclical process from normal operation to initial failure, from failure to replacement, and from replacement to normal operation. Therefore, in a specific implementation of the above-described embodiment, a period of time before a first time when the battery satisfies the faulty replacement condition and a time interval between the first time and a second time when the battery is replaced from the first time may be taken as the decay time interval.

In a specific implementation of an embodiment, the determining the battery state of the battery at each historical time according to the historical degradation time interval of the battery may be implemented by steps shown in fig. 8.

Referring to fig. 8, a detailed flowchart illustrating the determination of the battery status of the battery at various historical times according to an embodiment of the present application may specifically include steps 3554 to 3555:

and step 3554, determining the battery state at each moment in the decline time interval as an early warning state.

In step 3555, the battery state at each time in the time intervals other than the decline time interval is determined as a healthy state.

Specifically, in a specific implementation of this embodiment, the states of the battery at different times may be marked in a labeling form, and a specific labeling rule may be:

firstly, determining a first time t when the battery meets a fault replacement condition according to the acquired data₁And a second time t for replacing the battery₂And the time t when the data of the battery is stable after replacement₃. Then, the battery is put at t₁-T_decayTo t₂Each time within the time interval of (a) is marked as an "early warning" status label. Finally, the battery is put in the time t₁-T_decay-1 preceding time interval and said battery at time t₃Each time within the following time interval is marked as a "healthy" status label.

Note that T in the above description_decayCan be used to describe the performance decay time that the battery has experienced before entering a fault state, and can be determined based on field expert experience.

In a specific implementation of an embodiment, determining the battery state at each time within the decay time interval as the early warning state may be implemented by the steps shown in fig. 9.

Referring to fig. 9, a detailed flowchart illustrating the determination of the battery state as the early warning state at each time in the fading time interval according to an embodiment of the present application is shown, which may specifically include steps 35541 to 35542:

and 35541, determining the sub-decay time intervals in the decay time interval, wherein the decay degree of the battery in different sub-decay time intervals is different.

And 35542, determining the early warning state grade of the battery in different sub-fading time intervals according to the fading degree of the battery in different sub-fading time intervals.

It is understood that in a specific implementation of the present embodiment, the early warning state of the battery in the decay time interval may be measured more finely by the early warning state level. In the present application, the warning state or the like may be consideredThe higher the level, the higher the probability that the battery is unhealthy, i.e., the more urgent is the early warning state. Specifically, for example, the early warning state may be classified into four levels of "1, 2, 3, 4", and the fading time interval may be divided into four sub-intervals, wherein the second time t when the battery is replaced is a distance₂The closer the subinterval, the higher the probability that the battery is in an unhealthy state in the subinterval, i.e., the higher the early warning state level.

Step 3570, training and generating the battery state monitoring model based on the first characteristic parameter and/or the second characteristic parameter of the battery at each historical moment and the battery state at each historical moment.

In order to facilitate training and generating the battery state monitoring model, in a specific implementation of the above embodiment, different parameters may be assigned to different states of the battery, where the parameters may be expected probability values for characterizing the battery to have an unhealthy state. For example, the "healthy" status label of the battery may be assigned an expected probability value of less than 50%, the "early warning" status label of the battery may be assigned an expected probability value of more than 50% and less than 100%, further, the "1-level early warning" status label of the battery may be assigned an expected probability value of more than 50% and less than 70%, and the "4-level early warning" status label of the battery may be assigned an expected probability value of more than 90% and less than 100%. In one embodiment of the present application, the battery state monitoring model may be trained and generated with a supervised learning model as an initial model.

Referring to FIG. 10, a schematic diagram of a supervised learning approach is illustrated, according to one embodiment of the present application.

As shown in fig. 10, supervised learning is a common method in machine learning, and each training sample thereof is composed of an "input vector" and a corresponding "expected output". Based on these "input-output pairs" and with the help of certain algorithms, a pattern (function) can be created that maps the inputs to the outputs, from which new instances are inferred.

In a formal language expression, the language expression is composed of N samplesIs { (x)₁,y₁)，……，(x_N,y_N) In which x_iIs the "feature vector" (i.e., input vector) of the ith sample, y_iIs the "label" (i.e., expected output value) of the ith sample. Supervised learning methods attempt to find a function g:

make the loss function

Minimization, where X ═ X₁,x₂… …) is the input space, Y ═ Y (Y)₁,y₂… …) is the corresponding real output of the input space,

is the output space of the function g. The supervised learning method cyclically executes an optimization algorithm, updating model parameters to modify the representation of the function g such that a loss function is generated

And is continuously decreased until a certain pre-specified condition is reached.

In one embodiment of the present application, the input of the model is the first characteristic parameter and/or the second characteristic parameter, and the output is the predicted probability value of the training sample state being "early warning". In the training process, the model continuously updates parameters through a certain optimization algorithm to reduce the error between the predicted probability value and the expected probability value until a certain set condition is met, the training is completed, and the battery state monitoring model is generated.

In other embodiments of the present application, the initial model for training the battery state monitoring model may also be a support vector machine-based model such as an SVM, a linear regression-based model such as LR, a Logistic regression, a naive bayes model, a linear discriminant analysis-based model such as LDA, a decision tree-based model such as GBDT, a neural network-based model such as ANN, a distance metric-based model such as KNN, or a hybrid or modified version of the above models.

Finally, in step 350, the first characteristic parameter and/or the second characteristic parameter of the battery are input into a battery state monitoring model generated through the above-mentioned process training in advance, and a battery state parameter, that is, a predicted probability value meeting the error requirement is output.

With continued reference to fig. 2, in step 370, the state of the battery is monitored based on the battery state parameter.

Specifically, the state of the battery may be monitored according to the output battery state parameter. For example, in the foregoing embodiment, if the output battery state parameter (predicted probability value) is a predicted probability value greater than 90% and less than 100%, it indicates that the battery is in the "level 4 warning" state.

It is noted that the significance of the expected/predicted probability values as described above is: for measuring the likelihood of an unhealthy condition of the battery. When the probability value exceeds a threshold value, that is, when the battery is more than a certain degree of unhealthy, the battery may be considered to enter an "early warning" state and needs to be early warned, for example, when the expected probability value of the battery is greater than 90% and less than 100%, the battery needs to be early warned at level 4.

In order to make those skilled in the art understand the present invention more, the following will describe the technical solution of the present application with specific scene embodiments:

referring to fig. 11, a schematic view of a scenario of a battery status monitoring method according to an embodiment of the present application is shown.

As shown in the figure, firstly, a control center in a scene collects initial characteristic data of a UPS battery installed in a database through a collection module, and a processing module and an extraction module are used for processing and extracting the initial characteristic parameters to obtain first characteristic parameters. On one hand, the historical first characteristic parameters can be further processed to obtain 10-dimensional characteristic parameters of the battery and state labels corresponding to the 10-dimensional characteristic parameters, and the state labels are used for training a supervised learning model to obtain a battery state monitoring model arranged in the detection module. On the other hand, the real-time first characteristic parameter is further processed to obtain a real-time 10-dimensional characteristic parameter of the battery, the real-time 10-dimensional characteristic parameter of the battery is input into the battery state monitoring model arranged in the detection module, the real-time battery state parameter can be obtained, and the real-time monitoring of the battery is further realized,

the advantages of the embodiment of the scene are as follows:

and (4) economy. The invention realizes the automation of the whole process, monitors and processes data on line, does not need to establish a mathematical model manually, and greatly saves the labor cost on site and in technology.

And (5) practicability. The invention only uses the voltage, the internal resistance and the temperature of the battery in the floating charge state to evaluate the health degree, accords with the practical conditions of less charge-discharge time ratio and low data acquisition dimensionality in the use field, and is suitable for the use scenes of most energy storage batteries including UPS batteries

And (4) portability. The invention does not establish an evaluation model in advance, but learns from the collected battery historical data, and is not limited to a certain specific battery model or use scene.

And (6) reliability. The invention adopts a supervised learning mode to carry out data mining, and labels the training set according to the expert experience, thereby ensuring the accuracy of the final model.

Convenience is provided. The invention does not need to additionally install other equipment, the on-site battery polling instrument is used as an acquisition module, and other modules are realized in a computer of a control center.

Embodiments of the apparatus of the present application are described below, which may be used to perform the battery condition monitoring methods of the above-described embodiments of the present application. For details that are not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the battery state monitoring method described above in the present application.

FIG. 12 shows a block diagram of a battery condition monitoring device according to an embodiment of the present application.

Referring to fig. 12, a battery state monitoring apparatus 1200 according to an embodiment of the present application includes: an acquisition unit 1201, a determination unit 1202, an output unit 1203, and a monitoring unit 1204.

The acquiring unit 1201 is used for acquiring a first characteristic parameter of the battery; a determining unit 1202, configured to determine a second characteristic parameter of the battery according to the first characteristic parameter of the battery; an output unit 1203, configured to input the first characteristic parameter and/or the second characteristic parameter of the battery into a battery state monitoring model trained in advance, so as to output a battery state parameter; a monitoring unit 1204, configured to monitor a state of the battery based on the battery state parameter.

In some embodiments of the present application, based on the foregoing scheme, the obtaining unit 1201 is configured to: acquiring first characteristic parameters of the battery at historical moments and historical decline time intervals of the battery; the determining unit 1202 is configured to: determining second characteristic parameters of the battery at various historical moments according to the first characteristic parameters, and determining battery states of the battery at various historical moments according to decay time intervals of the battery at historical moments; the battery state monitoring device further includes: and the model training unit is used for training and generating the battery state monitoring model based on the first characteristic parameter and/or the second characteristic parameter of the battery at each historical moment and the battery state at each historical moment.

In some embodiments of the present application, based on the foregoing scheme, the obtaining unit 1201 is configured to: acquiring initial characteristic parameters of the battery at various historical moments; detecting whether the initial characteristic parameters are abnormal or not; and acquiring the initial characteristic parameter without the abnormality as a first characteristic parameter.

In some embodiments of the present application, based on the foregoing scheme, the obtaining unit 1201 is configured to: and acquiring the current value and/or the voltage value and/or the internal resistance value and/or the temperature value of the battery at various historical moments.

In some embodiments of the present application, based on the foregoing solution, the first characteristic parameter of the battery at each historical time includes a current value of the battery at each historical time, and the obtaining unit 1201 is configured to: before determining second characteristic parameters of the battery at historical moments according to the first characteristic parameters, detecting whether current values of the battery at historical moments are larger than a preset threshold value; and filtering a first characteristic parameter corresponding to the moment when the current value is greater than a preset threshold value.

In some embodiments of the present application, based on the foregoing solution, the first characteristic parameter of the battery at each historical time includes a voltage value and/or an internal resistance value of the battery at each historical time, and the determining unit 1202 is configured to: and determining the relative voltage value and/or the relative internal resistance value and/or the voltage change value and/or the internal resistance change value and/or the voltage gradient value and/or the internal resistance gradient value and/or the voltage internal resistance ratio of the battery at each historical moment according to the voltage value and/or the internal resistance value of the battery at each historical moment.

In some embodiments of the present application, based on the foregoing scheme, the obtaining unit 1201 is configured to: determining a first moment when the battery meets a fault replacement condition and a second moment when the battery is replaced; determining a performance decay time experienced before the battery enters a fault state; and determining a historical degradation time interval of the battery according to the first time and the second time and the performance degradation time before the battery enters the fault state.

In some embodiments of the present application, based on the foregoing scheme, the determining unit 1202 is configured to: determining the battery state at each moment in the decline time interval as an early warning state; and determining the battery state at each moment in other time intervals except the decline time interval as the state of health.

In some embodiments of the present application, based on the foregoing scheme, the determining unit 1202 is configured to: determining sub-fading time intervals in the fading time interval, wherein the fading degrees of the battery in different sub-fading time intervals are different; and determining the early warning state grade of the battery in different sub-fading time intervals according to the fading degree of the battery in different sub-fading time intervals.

It should be noted that the computer system 1300 of the electronic device shown in fig. 13 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 13, a computer system 1300 includes a Central Processing Unit (CPU)1301 that can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 1302 or a program loaded from a storage portion 1308 into a Random Access Memory (RAM) 1303. In the RAM 1303, various programs and data necessary for system operation are also stored. The CPU 1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An Input/Output (I/O) interface 1305 is also connected to bus 1304.

The following components are connected to the I/O interface 1305: an input portion 1306 including a keyboard, a mouse, and the like; an output section 1307 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage portion 1308 including a hard disk and the like; and a communication section 1309 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 1309 performs communication processing via a network such as the internet. A drive 1310 is also connected to the I/O interface 1305 as needed. A removable medium 1311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1310 as necessary, so that a computer program read out therefrom is mounted into the storage portion 1308 as necessary.

In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications component 1309 and/or installed from removable media 1311. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 1301.

It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. 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 or flowchart illustration, and combinations of blocks in the block diagrams 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.

The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the above embodiments.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A battery condition monitoring method, the monitoring method comprising:

acquiring a first characteristic parameter of the battery, wherein the first characteristic parameter comprises at least one of a voltage value and an internal resistance value without abnormality;

when the first characteristic parameter comprises a voltage value, determining a second characteristic parameter of the battery according to the first characteristic parameter of the battery, wherein the second characteristic parameter comprises a relative voltage value and/or a voltage change value and/or a voltage gradient value;

when the first characteristic parameter comprises an internal resistance value, determining a second characteristic parameter of the battery according to the first characteristic parameter of the battery, wherein the second characteristic parameter comprises a relative internal resistance value and/or an internal resistance change value and/or an internal resistance gradient value;

when the first characteristic parameters comprise a voltage value and an internal resistance value, determining second characteristic parameters of the battery according to the first characteristic parameters of the battery, wherein the second characteristic parameters comprise a relative voltage value and/or a relative internal resistance value and/or a voltage change value and/or an internal resistance change value and/or a voltage gradient value and/or an internal resistance gradient value and/or a voltage internal resistance ratio value;

inputting the first characteristic parameter and the second characteristic parameter of the battery into a battery state monitoring model trained in advance, and outputting a battery state parameter;

monitoring a state of the battery based on the battery state parameter;

the battery state monitoring model may be obtained by:

acquiring first characteristic parameters of the battery at various historical moments, and determining second characteristic parameters of the battery at various historical moments according to the first characteristic parameters;

acquiring historical decline time intervals of a battery, and determining battery states of the battery at historical moments according to the historical decline time intervals of the battery;

and training a machine learning model to generate the battery state monitoring model based on the first characteristic parameter and the second characteristic parameter of the battery at each historical moment and the battery state at each historical moment.

2. The method of claim 1, wherein obtaining the first characteristic parameter of the battery at each historical time comprises:

acquiring initial characteristic parameters of the battery at various historical moments;

detecting whether the initial characteristic parameters are abnormal or not;

and acquiring the initial characteristic parameter without the abnormality as a first characteristic parameter.

3. The method of claim 2, wherein the collecting initial characteristic parameters of the battery at various historical times comprises:

and collecting at least one of a voltage value and an internal resistance value of the battery at various historical moments.

4. The method of claim 1, wherein the first characteristic parameter of the battery at each historical time comprises a current value of the battery at each historical time,

before determining second characteristic parameters of the battery at various historical time instants according to the first characteristic parameters, the method further comprises:

detecting whether the current value of the battery at each historical moment is larger than a preset threshold value;

and filtering a first characteristic parameter corresponding to the moment when the current value is greater than a preset threshold value.

5. The method according to claim 1, wherein the first characteristic parameter of the battery at each historical time includes at least one of a voltage value and an internal resistance value of the battery at each historical time,

the determining a second characteristic parameter of the battery at each historical time according to the first characteristic parameter includes:

when the first characteristic parameter comprises a voltage value, determining a relative voltage value and/or a voltage change value and/or a voltage gradient value of the battery at each historical moment according to the voltage value of the battery at each historical moment;

when the first characteristic parameter comprises an internal resistance value, determining a relative internal resistance value and/or an internal resistance change value and/or an internal resistance gradient value of the battery at each historical moment according to the internal resistance value of the battery at each historical moment;

when the first characteristic parameter comprises a voltage value and an internal resistance value, determining a relative voltage value and/or a relative internal resistance value and/or a voltage variation value and/or an internal resistance variation value and/or a voltage gradient value and/or an internal resistance gradient value and/or a voltage internal resistance ratio of the battery at each historical moment according to the voltage value and the internal resistance value of the battery at each historical moment.

6. The method of claim 1, wherein obtaining historical decay time intervals of the battery comprises:

determining a first moment when the battery meets a fault replacement condition and a second moment when the battery is replaced;

determining a performance decay time experienced before the battery enters a fault state;

and determining a historical degradation time interval of the battery according to the first time and the second time and the performance degradation time before the battery enters the fault state.

7. The method of claim 1, wherein determining the battery state of the battery at historical times according to historical decay time intervals of the battery comprises:

determining the battery state at each moment in the decline time interval as an early warning state;

and determining the battery state at each moment in other time intervals except the decline time interval as the state of health.

8. The method of claim 7, wherein determining the battery status at each time within the decay time interval as an early warning status comprises:

determining sub-fading time intervals in the fading time interval, wherein the fading degrees of the battery in different sub-fading time intervals are different;

and determining the early warning state grade of the battery in different sub-fading time intervals according to the fading degree of the battery in different sub-fading time intervals.

9. A battery condition monitoring device, comprising:

an acquisition unit configured to acquire a first characteristic parameter of a battery, the first characteristic parameter including at least one of a voltage value and an internal resistance value at which there is no abnormality;

the determining unit is used for determining a second characteristic parameter of the battery according to the first characteristic parameter of the battery when the first characteristic parameter comprises a voltage value, wherein the second characteristic parameter comprises a relative voltage value and/or a voltage change value and/or a voltage gradient value; when the first characteristic parameter comprises an internal resistance value, determining a second characteristic parameter of the battery according to the first characteristic parameter of the battery, wherein the second characteristic parameter comprises a relative internal resistance value and/or an internal resistance change value and/or an internal resistance gradient value; when the first characteristic parameters comprise a voltage value and an internal resistance value, determining second characteristic parameters of the battery according to the first characteristic parameters of the battery, wherein the second characteristic parameters comprise a relative voltage value and/or a relative internal resistance value and/or a voltage change value and/or an internal resistance change value and/or a voltage gradient value and/or an internal resistance gradient value and/or a voltage internal resistance ratio value;

the output unit is used for inputting the first characteristic parameter and the second characteristic parameter of the battery into a battery state monitoring model trained in advance and outputting a battery state parameter;

a monitoring unit for monitoring the state of the battery based on the battery state parameter;

the acquisition unit is further configured to: acquiring first characteristic parameters of the battery at historical moments and historical decline time intervals of the battery;

the determination unit is further configured to: determining second characteristic parameters of the battery at various historical moments according to the first characteristic parameters, and determining battery states of the battery at various historical moments according to decay time intervals of the battery at historical moments;

the battery state monitoring device further includes: and the model training unit is used for training a machine learning model to generate the battery state monitoring model based on the first characteristic parameter and the second characteristic parameter of the battery at each historical moment and the battery state at each historical moment.

10. The apparatus of claim 9, wherein the obtaining unit is configured to: acquiring initial characteristic parameters of the battery at various historical moments; detecting whether the initial characteristic parameters are abnormal or not; and acquiring the initial characteristic parameter without the abnormality as a first characteristic parameter.

11. The apparatus of claim 10, wherein the obtaining unit is configured to: and collecting at least one of a voltage value and an internal resistance value of the battery at various historical moments.

12. The apparatus according to claim 9, wherein the first characteristic parameter of the battery at each historical time comprises a current value of the battery at each historical time, and the obtaining unit is configured to: before determining second characteristic parameters of the battery at historical moments according to the first characteristic parameters, detecting whether current values of the battery at historical moments are larger than a preset threshold value; and filtering a first characteristic parameter corresponding to the moment when the current value is greater than a preset threshold value.

13. The apparatus according to claim 9, wherein the first characteristic parameter of the battery at each historical time includes at least one of a voltage value and an internal resistance value of the battery at each historical time, and the determination unit is configured to: when the first characteristic parameter comprises a voltage value, determining a relative voltage value and/or a voltage change value and/or a voltage gradient value of the battery at each historical moment according to the voltage value of the battery at each historical moment; when the first characteristic parameter comprises an internal resistance value, determining a relative internal resistance value and/or an internal resistance change value and/or an internal resistance gradient value of the battery at each historical moment according to the internal resistance value of the battery at each historical moment; when the first characteristic parameter comprises a voltage value and an internal resistance value, determining a relative voltage value and/or a relative internal resistance value and/or a voltage variation value and/or an internal resistance variation value and/or a voltage gradient value and/or an internal resistance gradient value and/or a voltage internal resistance ratio of the battery at each historical moment according to the voltage value and the internal resistance value of the battery at each historical moment.

14. The apparatus of claim 9, wherein the obtaining unit is configured to: determining a first moment when the battery meets a fault replacement condition and a second moment when the battery is replaced; determining a performance decay time experienced before the battery enters a fault state; and determining a historical degradation time interval of the battery according to the first time and the second time and the performance degradation time before the battery enters the fault state.

15. The apparatus of claim 9, wherein the determining unit is configured to: determining the battery state at each moment in the decline time interval as an early warning state; and determining the battery state at each moment in other time intervals except the decline time interval as the state of health.

16. The apparatus of claim 15, wherein the determining unit is configured to: determining sub-fading time intervals in the fading time interval, wherein the fading degrees of the battery in different sub-fading time intervals are different; and determining the early warning state grade of the battery in different sub-fading time intervals according to the fading degree of the battery in different sub-fading time intervals.

17. A computer-readable storage medium, on which a computer program is stored, the computer program comprising executable instructions that, when executed by a processor, carry out the method of any one of claims 1 to 8.

18. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is arranged to execute the executable instructions to implement the method of any one of claims 1 to 8.