CN115840155B

CN115840155B - Battery status detection method, device, computer equipment, storage medium and product

Info

Publication number: CN115840155B
Application number: CN202211191958.4A
Authority: CN
Inventors: 孙龙; 谢岚; 林真; 李伟; 吴凯
Original assignee: Contemporary Amperex Technology Co Ltd
Current assignee: Contemporary Amperex Technology Co Ltd
Priority date: 2022-09-28
Filing date: 2022-09-28
Publication date: 2025-08-05
Anticipated expiration: 2042-09-28
Also published as: CN115840155A

Abstract

The present application relates to a battery status detection method, device, computer equipment, storage medium and product. The method comprises: obtaining low-frequency noise test data of a battery, and determining the detection result of the battery under the target status indicator based on the low-frequency noise test data and the detection strategy of the target status indicator. The present method can obtain low-frequency noise test data of a battery through a low-frequency noise test method. The low-frequency noise test method is relatively sensitive to the test data of the battery and has a high test efficiency, thereby improving the detection efficiency of the battery status. At the same time, a variety of different battery status indicators are detected. Different battery status indicators reflect the health status of the battery in different usage scenarios. In this way, a variety of different battery status indicators can comprehensively reflect the health status of the battery, thereby improving the comprehensiveness of the battery status detection results.

Description

Battery state detection method, device, computer equipment, storage medium and product

Technical Field

The present application relates to the field of battery technologies, and in particular, to a battery state detection method, apparatus, computer device, storage medium, and product.

Background

With the continuous development of new energy technology, batteries are increasingly widely applied in production and life, so that the research on the health state and the service life of the batteries is more and more important.

Taking the state of health of a battery as an example, in the related art, the state of health of the battery is mainly detected by an electrical test method such as charge and discharge.

However, when detecting the state of health of the battery in the related art, the detection efficiency is too low and the detection result is not comprehensive enough.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a battery state detection method, apparatus, computer device, storage medium, and product that can improve the efficiency of detecting the state of health of a battery and the comprehensiveness of the detection result.

In a first aspect, an embodiment of the present application provides a battery state detection method, including:

Acquiring low-frequency noise test data of a battery;

and determining a detection result of the battery under the target state indexes according to the low-frequency noise test data and the detection strategy of the target state indexes, wherein the target state indexes comprise a plurality of different battery state indexes.

According to the battery state detection method, low-frequency noise test data of the battery are obtained, and the detection result of the battery under the target state index is determined according to the low-frequency noise test data and the detection strategy of the target state index. In addition, the method can detect various battery state indexes at the same time, and the different battery state indexes reflect the health states of the batteries in different use scenes, so that the various battery state indexes can comprehensively reflect the health states of the batteries, and the comprehensiveness of battery state detection results is improved. And the same detection strategy can be set for different battery state indexes, and different detection strategies can be set, so that the flexible and rich detection strategies ensure the diversity of battery state detection modes. In addition, the method adopts the low-frequency noise test data to analyze and detect, and any type of battery can acquire the low-frequency noise test data, so that the battery state detection method can be suitable for detecting the states of multiple types of batteries, and the wide applicability of the battery state detection method is improved.

In one embodiment, determining a detection result of the battery under the target state index according to the low-frequency noise test data and the detection strategy of the target state index includes:

Acquiring a noise evaluation value of a target state index according to the low-frequency noise test data, wherein the noise evaluation value represents the noise level of the healthy state of the battery under the target state index;

And determining the detection result of the battery under the target state index according to the noise evaluation value and the detection strategy of the target state index.

According to the technical scheme, the noise evaluation value of the target state index can be obtained based on the low-frequency noise test data obtained by the low-frequency noise test method, the detection result of the battery under the target state index can be determined through the noise evaluation value by adopting the detection strategy corresponding to the target state index.

In one embodiment, obtaining the noise evaluation value of the target state index according to the low-frequency noise test data includes:

Performing frequency domain conversion processing on the low-frequency noise test data to obtain noise power spectrum data of the battery;

A noise evaluation value of the target state index is determined from the noise power spectrum data of the battery.

According to the technical scheme, the low-frequency noise test data can be subjected to frequency domain conversion processing to obtain the noise power spectrum data of the battery, and the noise evaluation value of the target state index is determined according to the noise power spectrum data of the battery; according to the method, time domain data of the battery obtained through testing can be converted into frequency domain data, namely low-frequency noise test data is converted into noise power spectrum data, and a frequency domain evaluation value corresponding to the battery under a target state index, namely the noise evaluation value, is obtained through the frequency domain data, and the battery state detection process is further completed based on the frequency domain evaluation value, so that the battery state detection process does not need to solve a complex calculus equation, and can be realized through a simple processing process, so that the operand in the battery state detection process can be reduced, and the speed and the efficiency of the battery state detection are improved.

In one embodiment, the noise power spectrum data comprises a power spectrum density frequency curve at a single voltage or a single current, and determining the noise evaluation value of the target state index from the noise power spectrum data of the battery comprises at least one of:

According to the power spectral density frequency curve, determining a power spectral density value at a specified frequency as a noise evaluation value of a target state index;

According to the power spectral density frequency curve, determining the amplitude of a power spectral density value in a first preset frequency range as a noise evaluation value of a target state index;

Determining the turning frequency of the power spectrum density frequency curve as a noise evaluation value of a target state index;

And determining the slope of the power spectral density frequency curve in a second preset frequency range as a noise evaluation value of the target state index.

According to the technical scheme provided by the embodiment of the application, the noise evaluation value of the target state index can be determined in various modes according to the acquired power spectral density frequency curve of the battery under the single voltage or current, so that the noise evaluation value of the target state index can be determined in an optimal mode according to the actual application requirement, the speed and the efficiency of determining the noise evaluation value of the target state index are improved, and the determination process of the noise evaluation value is simplified.

In one embodiment, the noise power spectrum data comprises a plurality of power spectrum density frequency curves at a plurality of different voltages or a plurality of different currents, and determining the noise evaluation value of the target state index according to the noise power spectrum data of the battery comprises at least one of the following modes:

according to the variation among the power spectral density values at the designated frequency in the power spectral density frequency curves, determining a noise evaluation value of the target state index;

according to the variation among the magnitudes of the power spectral density values in the third preset frequency range in the power spectral density frequency curves, determining a noise evaluation value of the target state index;

Determining a noise evaluation value of the target state index according to the variation among turning frequencies of the power spectrum density frequency curves;

And determining a noise evaluation value of the target state index according to the change amount between slopes in a fourth preset frequency range in the power spectrum density frequency curves.

According to the technical scheme, the noise evaluation value of the target state index can be determined in a plurality of modes according to the acquired power spectrum density frequency curves of the battery under a plurality of different voltages or a plurality of different currents, so that the noise evaluation value of the target state index can be determined in an optimal mode according to actual application requirements, the speed and efficiency of determining the noise evaluation value of the target state index are improved, the determination process of the noise evaluation value is simplified, the noise evaluation value of the target state index can be determined through the power spectrum density frequency curves of the battery under a single voltage or a single current, the noise evaluation value of the target state index can be determined through the power spectrum density frequency curves of the battery under a plurality of different voltages or a plurality of different currents, the diversity of the determination modes of the noise evaluation value of the target state index is increased, and the mode of determining the noise evaluation value of the target state index is more flexible.

In one embodiment, obtaining a noise evaluation value of a target state index according to low frequency noise test data includes:

A noise evaluation value for determining the low-frequency noise test data as a target state index, or

And determining a noise evaluation value of the target state index according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located.

According to the technical scheme provided by the embodiment of the application, the noise evaluation value of the target state index can be determined in different modes according to actual demands, so that the process of determining the noise evaluation value of the target state index is flexible.

In one embodiment, determining the noise evaluation value of the target state index according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located includes:

determining a noise correlation coefficient between the battery and adjacent batteries of the battery according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries;

And determining the noise correlation coefficient as a noise evaluation value of the target state index.

According to the technical scheme, the noise correlation coefficient between the battery and the adjacent battery of the battery can be determined according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries, and the noise correlation coefficient is determined to be the noise evaluation value of the target state index, so that the method can determine the noise evaluation value of the target state index in an average value mode, can determine the noise evaluation value of the target state index in a mode of calculating the noise correlation coefficient between the battery and the adjacent battery of the battery, the determination mode of the noise evaluation value of the target state index is further increased, the mode of determining the noise evaluation value of the target state index is more flexible, and meanwhile, the method does not need to consider the test data of other batteries in a module when determining the noise evaluation value of the target state index, and therefore accuracy of the noise evaluation value can be improved.

In one embodiment, determining a detection result of the battery under the target state index according to the noise evaluation value and the detection strategy of the target state index includes:

Acquiring a noise critical condition corresponding to the battery in a target critical state;

And determining the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition.

According to the technical scheme provided by the embodiment of the application, the corresponding noise critical condition of the battery in the target critical state can be obtained, and the detection result of the battery in the target state index is determined according to the noise evaluation value and the noise critical condition of the battery in the ideal state, so that the determined detection result is more accurate, and the accuracy of the detection result of the battery in the target state index is improved.

In one embodiment, determining the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition includes:

if the noise evaluation value does not meet the noise critical condition, determining that the detection result of the battery under the target state index is abnormal;

If the noise evaluation value meets the noise critical condition, determining that the detection result of the battery under the target state index is normal.

In the technical scheme of the embodiment of the application, the detection result of the battery under the target state index can be determined by comparing the noise evaluation value with the noise critical condition, and the accuracy of the detection result can be improved because the noise critical condition is the noise critical condition of the battery in the ideal state.

In one embodiment, the noise critical condition is a noise threshold, the method further comprising:

Acquiring low-frequency noise test data of a battery and low-frequency noise test average values of low-frequency noise test data of other batteries in a module where the battery is located;

The low frequency noise test average value is determined as the noise threshold value.

According to the technical scheme, on the basis of acquiring the low-frequency noise test data of the battery, the low-frequency noise test data of other batteries in the module where the battery is located can be acquired, the low-frequency noise test data of the battery and the low-frequency noise test average value between the low-frequency noise test data of the other batteries are acquired, and then the low-frequency noise test average value is determined as the noise threshold value.

In one embodiment, the noise critical condition includes a first threshold, a second threshold, and a third threshold;

According to the noise evaluation value and the noise critical condition, determining a detection result of the battery under the target state index comprises the following steps:

if the noise evaluation value exceeds the first threshold value and does not exceed the second threshold value, determining that the detection result of the battery under the target state index is suspected abnormal;

If the noise evaluation value exceeds the second threshold value and does not exceed the third threshold value, determining that the detection result of the battery under the target state index is slightly abnormal;

if the noise evaluation value exceeds the third threshold value, the detection result of the battery under the target state index is determined to be severely abnormal.

According to the technical scheme provided by the embodiment of the application, the abnormal detection results of different degrees of the battery under the target state indexes can be determined by setting a plurality of different grade thresholds, so that the grade of the determined abnormal detection result is finer, and an accurate basis is provided for selecting effective solving measures when the abnormality is solved.

In one embodiment, the method further comprises:

acquiring a back-end processing strategy corresponding to the target state index;

If the detection result of the battery under the target state index is abnormal, the battery is processed according to the back-end processing strategy corresponding to the target state index.

According to the technical scheme, when the detection result is determined to be suspected abnormality, the detection result of the battery under the target state index can be confirmed again, so that the accuracy of the detection result is ensured, the problem that the normal battery is mishandled by adopting a back-end processing strategy can be avoided, resources are saved, the problem that the safety of the battery is low because the abnormal battery is not timely handled by adopting the back-end processing strategy can be avoided, meanwhile, the method can adopt corresponding back-end processing strategies to process the battery according to the abnormal detection results of different grades, the poor battery can be prevented from flowing into the market to cause safety accidents, the abnormal battery can be prevented from being normally used in the using stage to cause customer complaints, the safety of the battery is further improved, and the using experience of a user is improved.

In one embodiment, the anomalies include suspected anomalies, slight anomalies, and severe anomalies, and the processing of the battery according to a backend processing policy corresponding to the target state index includes:

If the detection result of the battery under the target state index is suspected to be abnormal, determining the detection result of the battery under the target state index in a secondary confirmation mode;

If the detection result of the battery under the target state index is slightly abnormal, repairing the battery through repairing operation;

if the detection result of the battery under the target state index is serious abnormality, the battery is scrapped through scrapping operation.

According to the technical scheme provided by the embodiment of the application, different back-end processing strategies can be adopted to correspondingly process abnormal batteries of different degrees, so that safety accidents are prevented in time, and the use experience of a user can be increased.

In one embodiment, acquiring low frequency noise test data of a battery includes:

acquiring voltage or current fluctuation data along with time in the discharging process of the battery with specific current through a pre-built low-frequency noise test system to obtain low-frequency noise test data, or

And detecting fluctuation data of voltage or current of the battery along with time through an integrated test chip to obtain low-frequency noise test data.

According to the technical scheme provided by the embodiment of the application, the low-frequency noise test data of the battery can be obtained in various modes, so that the flexibility of obtaining the low-frequency noise test data of the battery is improved.

In one embodiment, before acquiring the low frequency noise test data of the battery, the method further includes:

detecting whether the voltage of the battery meets the voltage consistency requirement;

If yes, executing the step of acquiring the low-frequency noise test data of the battery;

If not, the battery is regulated to meet the voltage consistency requirement through the charge-discharge equipment.

According to the technical scheme provided by the embodiment of the application, whether the voltages of the batteries meet the voltage consistency requirement can be detected, and when the voltages of the batteries in the module are not met, the voltages of the batteries in the module are set to be consistent, so that the detected battery states of all the batteries in the same module are accurate.

In a second aspect, an embodiment of the present application provides a battery state detection apparatus, including:

the test data acquisition module is used for acquiring low-frequency noise test data of the battery;

the detection result determining module is used for determining the detection result of the battery under the target state indexes according to the low-frequency noise test data and the detection strategy of the target state indexes, wherein the target state indexes comprise a plurality of different battery state indexes.

In a third aspect, the application also provides a computer device, the charge control device comprising a memory storing a computer program and a processor implementing the steps of the method of any of the first aspects described above when the computer program is executed by the processor.

In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of any of the first aspects described above.

In a fifth aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects described above.

The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.

Drawings

FIG. 1 is an application environment diagram of a battery state detection method in one embodiment;

FIG. 2 is a flow chart of a method of detecting battery status in one embodiment;

FIG. 3 is a flowchart of a battery state detection method according to another embodiment;

FIG. 4 is a flowchart of a battery state detection method according to another embodiment;

FIG. 5 is a graph of power spectral density versus frequency for a single voltage or current in another embodiment;

FIG. 6 is another power spectral density frequency plot at manufacturing defectivity according to another embodiment;

FIG. 7 is another power spectral density frequency plot at the self-discharge level in another embodiment;

FIG. 8 is a flowchart of a battery state detection method according to another embodiment;

FIG. 9 is a flowchart of a battery state detection method according to another embodiment;

FIG. 10 is a flowchart of a battery state detection method according to another embodiment;

FIG. 11 is a flowchart of a battery state detection method according to another embodiment;

FIG. 12 is a flowchart of a battery state detection method according to another embodiment;

FIG. 13 is a block diagram showing the structure of a battery state detecting device in one embodiment;

Fig. 14 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

Embodiments of the technical scheme of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and thus are merely examples, and are not intended to limit the scope of the present application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs, the terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting of the application, and the terms "comprising" and any variations thereof in the description of the application and the claims and the above description of the drawings are intended to cover non-exclusive inclusions.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

With the continuous development of new energy technology, batteries are increasingly widely used in production and life. Therefore, the state of health and the life of the battery are increasingly emphasized. Taking the state of health of the battery as an example, in the related art, the charge and discharge parameters of the battery can be detected by an electrical test method such as charge and discharge, and then the state of health of the battery can be evaluated based on the charge and discharge parameters of the battery, and further, defective batteries, such as poor K-value batteries, can be selected at the stage of battery manufacturing according to the state of health of the battery, and abnormal batteries, such as high self-discharge, lithium-precipitation batteries, internal short-circuit batteries, and the like, can be selected at the stage of battery use, so as to improve the safety of the battery at the stage of battery use.

The state of health of the battery is generally detected based on an electrical test method such as charge and discharge. However, the speed of detecting the charge and discharge parameters of the battery by using the electrical test methods such as charge and discharge is slow, and the electrical test methods are insensitive to the detection of the charge and discharge parameters, so that the charge and discharge parameters of the battery cannot be detected in time, and thus the state of health of the battery cannot be estimated in time, and the detection efficiency of the state of the battery is low. Meanwhile, the detection mode of the electrical test method such as charge and discharge is single, and the method is only suitable for detecting the state of a specific battery and detecting the state of the specific battery, so that the detection result of the state of the battery is not comprehensive.

Based on this, in order to solve the problems that the detection efficiency of the battery state is low and the detection result of the battery state is not comprehensive, the applicant has studied and put forward a battery state detection method, which not only can improve the detection efficiency of the battery state by detecting the battery state by a low-frequency noise test method, but also can realize the battery state detection by adopting different detection strategies for different types of batteries and for different detection indexes of the battery, thereby improving the comprehensiveness of the battery state detection result.

As shown in fig. 1, an application environment of the battery state detection method according to the embodiment of the present application includes a computer device 11, a low-frequency noise test system 12, and a battery 13 to be detected, where the low-frequency noise test system 12 includes an adjustable resistor 121, a low-frequency noise test device 122, and an amplifier 123, and the low-frequency noise test device 122 may be a low-frequency noise tester or an oscilloscope, etc. The battery 13, the adjustable resistor 121, the low-frequency noise testing device 122 and the amplifier 123 are electrically connected, and a specific connection manner is shown in fig. 1, and in the embodiment of the present application, the low-frequency noise testing device 122 is in communication connection with the computer device 11, and the connection manner may be bluetooth, a mobile network, wifi, etc. for the low-frequency noise testing device 122 to send the detected test data to the computer device 11. Alternatively, the battery 13 to be detected may be one or more batteries in a module, and a battery is illustrated in fig. 1, and the detection process is not limited to one battery. Meanwhile, in the process of detecting the battery, the battery is placed depending on a metal box, which is also illustrated in fig. 1.

It should be noted that, the method for detecting the battery state provided by the embodiment of the present application can be implemented by an application environment including a computer device, an integrated test chip and a battery to be detected, in addition to the application environment shown in fig. 1. The integrated test chip is integrated with the adjustable resistor, the low-frequency noise test device and the test chip of the amplifier in the low-frequency noise test system 12, and communication connection is formed between the computer device and the integrated test chip, so that the integrated test chip can send the detected test data to the computer device.

In an embodiment, fig. 2 is a schematic flow chart of a battery state detection method according to an embodiment of the present application, and in an embodiment of the present application, an execution subject of the method is illustrated as a computer device in fig. 1. As shown in fig. 2, the method according to the embodiment of the present application may include the following steps:

S100, acquiring low-frequency noise test data of the battery.

In the embodiment of the application, the computer equipment acquires the current low-frequency noise test data of the battery. The battery may be a battery made of different positive electrode materials, such as a ternary battery, an iron-lithium battery, etc., or may be an ion battery (such as a lithium ion battery, a sodium ion battery, a potassium ion battery, a zinc ion battery, etc.), a metal-based battery (such as a lithium metal battery, a sodium metal battery, a potassium metal battery, etc.), a non-negative electrode battery, etc., and the type of the battery is not limited in any way.

In one embodiment, the method for acquiring the low-frequency noise test data of the battery may be to directly detect the current low-frequency noise test data of the battery through the low-frequency noise detection device, and then the computer device acquires the current low-frequency noise test data of the battery detected by the low-frequency noise detection device in real time.

In still another embodiment, the obtaining the low-frequency noise test data of the battery may further be obtained by the computer device obtaining, from the local or cloud, the low-frequency noise test data of the battery stored in advance in a historical period, and then predicting the current low-frequency noise test data of the battery according to the low-frequency noise test data of the battery in the historical period.

S200, determining a detection result of the battery under the target state index according to the low-frequency noise test data and the detection strategy of the target state index. Wherein the target state index comprises a plurality of different battery state indexes.

In the embodiment of the application, the target state indexes comprise two different battery state indexes, namely a first state index and a second state index, wherein one of the first state index and the second state index represents an index affecting the health state of the battery in the manufacturing stage, and the other state index represents an index affecting the health state of the battery in the using stage.

In the embodiment of the application, the first state index is used for indicating the index which affects the state of health of the battery in the manufacturing stage, and the second state index is used for indicating the index which affects the state of health of the battery in the using stage, so that the first state index can be the maximum state of charge, the maximum energy state and the like of the battery in the manufacturing stage, and the second state index can also be the maximum state of charge, the maximum energy state and the like of the battery in the using stage.

Wherein the first state indicator may comprise a functional state or the like and the second state indicator may comprise a state of charge, a power state, a functional state, a remaining power, or the like. However, in the embodiment of the present application, the first state index is a manufacturing defect level, and the second state index is a self-discharge level, an aging level, a lithium precipitation level, and an internal short circuit level.

Alternatively, the detection policy of the target state index may be a detection policy constructed in advance. The battery states of the batteries of different types can be detected by adopting different detection strategies aiming at the same target state index, namely, the detection strategies corresponding to the batteries of different types aiming at the same target state index can be different, and the battery states of the batteries of different types can be detected by adopting the same detection strategy aiming at the same target state index, namely, the detection strategies corresponding to the batteries of different types can be the same.

Meanwhile, the battery with the same type can adopt different detection strategies to detect the battery states under different target state indexes, namely, the detection strategies corresponding to the battery with the same type by different target state indexes can be different, and the battery with the same type can adopt the same detection strategy to detect the battery states under different target state indexes, namely, the detection strategies corresponding to the battery with the same type by different target state indexes can be the same.

In addition, the same detection strategy can be adopted for detecting the battery states under different target state indexes of different types of batteries, that is, the detection strategies corresponding to the different types of batteries by the different target state indexes can be the same.

The detection strategy of the target state index can be an algorithm model detection method or a contrast analysis detection method and the like. Optionally, the method for determining the detection result of the battery under the target state index according to the low-frequency noise test data and the algorithm model detection method may be that an algorithm model corresponding to the target state index is trained in advance, the low-frequency noise test data obtained in the above steps is input into the algorithm model, and the detection result of the battery under the target state index is output through the algorithm model.

Optionally, the method for determining the detection result of the battery under the target state index according to the low-frequency noise test data and the comparative analysis detection method may be that a pre-constructed corresponding information base is obtained, the low-frequency noise data matched with the current low-frequency noise test data of the battery is searched in the corresponding information base, and the detection result corresponding to the matched low-frequency noise data is determined as the detection result of the battery under the target state index. Optionally, the corresponding information base may include low-frequency noise data of different types of batteries, different state indexes, detection results, and a corresponding relation among the three.

Here, the detection result of the battery under the target state index may be normal or abnormal, and the specific detection result is determined according to the actual state of the battery.

According to the battery state detection method, low-frequency noise test data of the battery are obtained, and the detection result of the battery under the target state index is determined according to the low-frequency noise test data and the detection strategy of the target state index. In addition, the method can detect various battery state indexes at the same time, and the different battery state indexes reflect the health states of the batteries in different use scenes, so that the various battery state indexes can comprehensively reflect the health states of the batteries, and the comprehensiveness of battery state detection results is improved. In addition, the same detection strategy can be set for different battery state indexes, and different detection strategies can be set, so that the flexible and rich detection strategies ensure the diversity of battery state detection modes. In addition, the method adopts the low-frequency noise test data to analyze and detect, and any type of battery can acquire the low-frequency noise test data, so that the battery state detection method can be suitable for detecting the states of multiple types of batteries, and the wide applicability of the battery state detection method is improved.

Based on the above embodiment, the procedure of determining the detection result of the battery under the target state index according to the above detection strategy of the low frequency noise test data and the target state index will be described below. In one embodiment, as shown in fig. 3, the step S200 includes the following steps:

S210, acquiring a noise evaluation value of the target state index according to the low-frequency noise test data, wherein the noise evaluation value represents the noise level of the healthy state of the battery under the target state index.

In the embodiment of the application, the noise evaluation value of the target state index is acquired according to the low-frequency noise test data and the target state index of the battery, and the noise evaluation value of the target state index is further processed to obtain the detection result of the battery under the target state index.

The noise evaluation value refers to the noise level of the health state of the battery under the target state index, and is equivalent to the basis for evaluating the health state of the battery under the target state index. For example, if the target state index is the battery aging degree, the noise evaluation value may be a parameter capable of accurately evaluating the battery aging degree. For another example, if the target state index is the degree of self-discharge of the battery, the noise evaluation value must be a parameter that can accurately evaluate the degree of self-discharge of the battery.

For example, the mode of obtaining the noise evaluation value of the target state index according to the low-frequency noise test data may be to perform format conversion processing on the low-frequency noise test data of the battery to obtain the noise evaluation value of the target state index. Or the mode of obtaining the noise evaluation value of the target state index according to the low-frequency noise test data can also be that the noise correlation index is calculated through the low-frequency noise test data of the battery, and the noise evaluation value of the target state index is determined according to the calculated noise correlation index. The noise correlation index may be noise strength, signal to noise ratio, noise signal power, etc.

S220, determining a detection result of the battery under the target state index according to the noise evaluation value and the detection strategy of the target state index.

Specifically, a detection strategy of a target state index constructed in advance can be obtained from a local or cloud, and the detection strategy of the target state index is adopted to correspondingly process the noise evaluation value of the battery under the target state index obtained in the above steps, so as to obtain a detection result of the battery under the target state index.

Or training a neural network detection model based on the detection strategy of the obtained target state index, inputting the noise evaluation value of the battery into the trained neural network detection model, and outputting the detection result of the battery under the target state index.

The battery state detection method in the embodiment of the application can obtain the noise evaluation value of the target state index based on the low-frequency noise test data obtained by the low-frequency noise test method, and determine the detection result of the battery under the target state index through the noise evaluation value by adopting the detection strategy corresponding to the target state index.

In order to simply and quickly obtain the noise evaluation value of the target state index, in one embodiment, as shown in fig. 4, the step of obtaining the noise evaluation value of the target state index according to the low-frequency noise test data in S210 may be implemented by the following steps:

s211, performing frequency domain conversion processing on the low-frequency noise test data to obtain noise power spectrum data of the battery.

In practical application, in some scenes, the operation amount involved in the time domain processing method is larger, and the related data of the battery cannot be accurately described by using limited parameters, so that the battery state detection speed is slower, the efficiency is lower and the accuracy of the detection result is not high enough, while the frequency domain processing method can decompose complex data into superposition of simple data, and can describe the characteristics of the battery more accurately.

The obtained low-frequency noise test data are time domain data, so that the computer equipment can firstly perform frequency domain conversion processing on the low-frequency noise test data by adopting a time-frequency conversion method to obtain noise power spectrum data of the battery, wherein the noise power spectrum data are frequency domain data. Alternatively, the time-frequency conversion method may be a laplace transform method or a Z transform method, but in the embodiment of the present application, the time-frequency conversion method is described as a fourier transform method.

S212, determining a noise evaluation value of the target state index according to the noise power spectrum data of the battery.

Here, the computer device may perform an index calculation process on the noise power spectrum data of the battery to obtain a noise evaluation value of the target state index. Meanwhile, the computer device may also directly determine the noise power spectrum data of the battery as the noise evaluation value of the target state index. In addition, the computer equipment can analyze and compare the noise power spectrum data of the battery to obtain a noise evaluation value of the target state index.

The battery state detection method in the embodiment of the application can perform frequency domain conversion processing on the low-frequency noise test data to obtain the noise power spectrum data of the battery, and determine the noise evaluation value of the target state index according to the noise power spectrum data of the battery; according to the method, time domain data of the battery obtained through testing can be converted into frequency domain data, namely low-frequency noise test data is converted into noise power spectrum data, and a frequency domain evaluation value corresponding to the battery under a target state index, namely the noise evaluation value, is obtained through the frequency domain data, and the battery state detection process is further completed based on the frequency domain evaluation value, so that the battery state detection process does not need to solve a complex calculus equation, and can be realized through a simple processing process, so that the operand in the battery state detection process can be reduced, and the speed and the efficiency of the battery state detection are improved.

In some situations, a single charge-discharge voltage or a single charge-discharge current of the battery may be set to implement a battery state detection process, and the following embodiments of the present application describe a process of determining a noise evaluation value of a target state index according to noise power spectrum data of the battery when the single charge-discharge voltage or the single charge-discharge current of the battery is set. In an embodiment, the noise power spectrum data includes a power spectrum density frequency curve under a single voltage or a single current, and the step of determining the noise evaluation value of the target state index according to the noise power spectrum data of the battery in S212 may include at least one of the following ways:

The method comprises the steps of determining a power spectral density value at a specified frequency as a noise evaluation value of a target state index according to a power spectral density frequency curve, determining an amplitude of the power spectral density value in a first preset frequency range as a noise evaluation value of the target state index according to the power spectral density frequency curve, determining a turning frequency of the power spectral density frequency curve as a noise evaluation value of the target state index, and determining a slope of the power spectral density frequency curve in a second preset frequency range as a noise evaluation value of the target state index.

In the embodiment of the application, the noise evaluation value of the target state index can be determined by selecting at least one dimension parameter from the four dimensions of parameters, wherein the power spectral density value at the specified frequency of the power spectral density frequency curve, the variation among the magnitudes of the power spectral density values in the first preset frequency range, the turning frequency and the slope in the second preset frequency range.

The process of determining the power spectral density value at the specified frequency as the noise evaluation value of the target state index from the power spectral density frequency curve will be described below.

In the low-frequency noise test process, a single charge-discharge voltage or a single charge-discharge current of the battery can be set to obtain low-frequency noise test data under the single voltage or the single current, and further a power spectral density frequency curve under the single voltage or the single current is obtained through the low-frequency noise test data under the single voltage or the single current.

The power spectral density frequency curve under a single voltage or a single current is a curve formed by the power spectral density corresponding to each frequency in a certain frequency range, a designated frequency can be selected from the frequency range, and the power spectral density value corresponding to the designated frequency on the power spectral density frequency curve is determined as a noise evaluation value of a target state index. Alternatively, the specified frequency may be any frequency within a frequency range corresponding to the power spectral density frequency curve.

For example, fig. 5 is a power spectral density frequency chart under a single voltage or a single current, each point on the power spectral density frequency chart corresponds to a set of values, including frequency and power spectral density values, respectively, and the frequency and power spectral density values in each set of values correspond to each other one by one, the power spectral density frequency chart in fig. 5 corresponds to a frequency range of [ f1, f2], and the designated frequency may be any frequency between f1 to f 2. If the designated frequency is f1, the corresponding power spectral density value is PSD1, if the designated frequency is f2, the corresponding power spectral density value is PSD2, if the designated frequency is f3, the corresponding power spectral density value is PSD3, and the power spectral density values obtained by other designated frequencies are similar, which will not be repeated.

The process of determining the amplitude of the power spectral density value in the first preset frequency range as the noise evaluation value of the target state index according to the power spectral density frequency curve will be described below.

Based on the power spectral density frequency curve under the single voltage or the single current obtained in the above steps, a power spectral density value in a first preset frequency range on the power spectral density frequency curve can be obtained first, then the power spectral density value in the first preset frequency range is subjected to processes such as averaging or median, so as to obtain an amplitude value of the power spectral density value in the first preset frequency range, and the amplitude value is determined as a noise evaluation value of a target state index.

In addition, based on the power spectrum density frequency curve under the single voltage or the single current obtained in the above step, a maximum power spectrum density value and a minimum power spectrum density value in a first preset frequency range on the power spectrum density frequency curve can be obtained first, then the maximum power spectrum density value and the minimum power spectrum density value in the first preset frequency range are differenced to obtain an amplitude value of the power spectrum density value in the first preset frequency range, and the amplitude value is determined as a noise evaluation value of the target state index.

Furthermore, based on the power spectrum density frequency curve under the single voltage or the single current obtained in the above steps, the power spectrum density value corresponding to each frequency in the first preset frequency range on the power spectrum density frequency curve can be obtained first, then the difference value between the power spectrum density values of every two frequencies in the first preset frequency range is calculated, and then all or part of the obtained frequency difference values are averaged to obtain the amplitude value of the power spectrum density value in the first preset frequency range, and the amplitude value is determined as the noise evaluation value of the target state index.

Continuing with the above example, if the first preset frequency range of the power spectral density frequency curve is [ f1, f2], the power spectral density value in the first preset frequency range may be the power spectral density value corresponding to each point on the power spectral density frequency curve between f 1-f 2. The power spectral density value corresponding to each frequency may be equal or unequal in the first preset frequency range from the frequency f1 to the frequency f2, which is not limited in this embodiment, and is not limited to the power spectral density value corresponding to each frequency illustrated in fig. 5.

For example, after a defective battery with a single voltage or a single current passes a low-frequency noise test under the same test environment, the obtained power spectral density frequency curve in the frequency range [ f1, f2] is shown in fig. 6, and correspondingly, fig. 6 also shows the power spectral density frequency curve of a normal battery in the frequency range [ f1, f2], so that the power spectral density values of the defective battery in the frequency range [ f1, f2] are larger than the power spectral density values of the normal battery.

In the same test environment, the power spectral density frequency curves of the battery with high self-discharge degree and the battery with low self-discharge degree under a single voltage or a single current in a certain frequency range are shown in fig. 7, correspondingly, fig. 7 also shows the power spectral density frequency curve of the battery (normal battery) in the self-discharge degree, so that the power spectral density values of the battery with high self-discharge degree in a certain frequency range are larger than those of the normal battery, and the power spectral density values of the normal battery in a certain frequency range are larger than those of the battery with low self-discharge degree.

In practical application, the battery with the self-discharge degree lower than the self-discharge degree belongs to the qualified battery, and the battery with the self-discharge degree higher than the self-discharge degree belongs to the unqualified battery.

The procedure of determining the turning frequency of the power spectral density frequency curve as the noise evaluation value of the target state index will be described below.

The turning frequency of the power spectrum density frequency curve represents a frequency value of a turning point corresponding to a diagonal line to an approximate horizontal line in the power spectrum density frequency curve.

In an embodiment, the method for determining the turning frequency of the power spectrum density frequency curve as the noise evaluation value of the target state index may be that all the power spectrum density values are sequentially obtained according to the order of the frequencies on the power spectrum density frequency curve, then the power spectrum density values corresponding to every two adjacent frequencies are compared, the turning point corresponding to the power spectrum density frequency curve is determined according to the comparison result, then the frequency corresponding to the turning point is determined as the turning frequency of the power spectrum density frequency curve, and the turning frequency is determined as the noise evaluation value of the target state index.

In another embodiment, the method of determining the turning frequency of the power spectral density frequency curve as the noise evaluation value of the target state index may further be that the slope of each point on the power spectral density frequency curve is calculated, then the slope of each two adjacent points is differenced to obtain a slope difference value, then one of the two points with the largest slope difference value is selected, and the frequency corresponding to the selected point is determined as the turning frequency of the power spectral density frequency curve, so as to obtain the noise evaluation value of the target state index.

The procedure of determining the slope of the power spectral density frequency curve in the second preset frequency range as the noise evaluation value of the target state index will be described below.

Based on the power spectral density frequency curve under the single voltage or the single current obtained in the above step, the slope of each point on the power spectral density frequency curve in the second preset frequency range can be calculated first, then the slope of all points in the second preset frequency range is averaged to obtain the slope of the power spectral density frequency curve in the second preset frequency range, and the slope of the power spectral density frequency curve in the second preset frequency range is determined as the noise evaluation value of the target state index.

Or based on the power spectral density frequency curve under the single voltage or the single current obtained in the step, any point on the power spectral density frequency curve in the second preset frequency range can be selected, the slope of the selected point is calculated, and the slope of the point is determined as the noise evaluation value of the target state index.

In the embodiment of the present application, the power spectral density frequency curve under the single voltage or the single current obtained in the above step is obtained by mathematically fitting all the power spectral density values within the second preset frequency range.

According to the battery state detection method, the noise evaluation value of the target state index can be determined in various modes according to the obtained power spectrum density frequency curve of the battery under the single voltage or current, so that the noise evaluation value of the target state index can be determined in an optimal mode according to actual application requirements, the speed and the efficiency of determining the noise evaluation value of the target state index are improved, and the determination process of the noise evaluation value is simplified.

In some situations, a plurality of different charge-discharge voltages or a plurality of different charge-discharge currents of the battery may be set to implement a battery state detection process, and the following embodiments of the present application will describe a process how to determine a noise evaluation value of a target state index according to noise power spectrum data of the battery when the plurality of different charge-discharge voltages or the plurality of different charge-discharge currents of the battery are set. In an embodiment, the noise power spectrum data includes a plurality of power spectrum density frequency curves with a plurality of different voltages or a plurality of different currents, and the step of determining the noise evaluation value of the target state index according to the noise power spectrum data of the battery in S212 may include at least one of the following ways:

The method comprises the steps of determining a noise evaluation value of a target state index according to the variation among power spectrum density values at a designated frequency in a plurality of power spectrum density frequency curves, determining a noise evaluation value of the target state index according to the variation among the magnitudes of the power spectrum density values in a third preset frequency range in the plurality of power spectrum density frequency curves, determining a noise evaluation value of the target state index according to the variation among turning frequencies of the plurality of power spectrum density frequency curves, and determining a noise evaluation value of the target state index according to the variation among slopes in a fourth preset frequency range in the plurality of power spectrum density frequency curves.

In the embodiment of the application, the noise evaluation value of the target state index can be determined by selecting at least one dimension parameter from the four dimension parameters, wherein the change between the power spectrum density values at the specified frequencies, the change between the magnitudes of the power spectrum density values in the third preset frequency range, the change between turning frequencies and the change between the slopes in the fourth preset frequency range in the plurality of power spectrum density frequency curves.

The process of determining the noise evaluation value of the target state index according to the amount of change between the power spectral density values at the specified frequency in the plurality of power spectral density frequency curves will be described.

In practical applications, each single voltage or each single current corresponds to one power spectral density frequency curve, and therefore, a plurality of different voltages or a plurality of different currents respectively correspond to a plurality of power spectral density frequency curves.

For the power spectral density frequency curve corresponding to each single voltage or each single current, the power spectral density value at the specified frequency can be obtained according to the power spectral density frequency curve. The method for obtaining the power spectral density value at the specified frequency according to the power spectral density frequency curve is similar to the method for obtaining the power spectral density value at the specified frequency according to the power spectral density frequency curve, and the description of the embodiment of the present application is omitted. Alternatively, the designated frequency in the present embodiment may be equal to or different from the designated frequency in the previous embodiment, which is not limited in any way.

Further, based on the obtained power spectrum density values at the designated frequency corresponding to each single voltage or each single current, the power spectrum density values in all the obtained power spectrum density values can be compared to obtain the maximum power spectrum density value and the minimum power spectrum density value in all the power spectrum density values, and the difference value between the maximum power spectrum density value and the minimum power spectrum density value is determined as the variation quantity between the power spectrum density values at the designated frequency corresponding to a plurality of different voltages or a plurality of different currents, so as to obtain the noise evaluation value of the target state index.

The process of determining the noise evaluation value of the target state index according to the amount of change between the magnitudes of the power spectral density values in the third preset frequency range in the plurality of power spectral density frequency curves will be described below.

Based on the power spectral density frequency curves corresponding to the different voltages or the different currents obtained in the steps, for each power spectral density frequency curve, the amplitude of the power spectral density value of the power spectral density frequency curve in the third preset frequency range can be obtained according to the power spectral density frequency curve. The method for obtaining the amplitude of the power spectral density value of the power spectral density frequency curve within the third preset frequency range according to the power spectral density frequency curve is similar to the method for obtaining the amplitude of the power spectral density value of the power spectral density frequency curve within the first preset frequency range according to the power spectral density frequency curve, and is not repeated in this embodiment of the present application. Optionally, the third preset frequency range in the present embodiment may be equal to or different from the first preset frequency range in the previous embodiment, which is not limited in any way.

Further, based on the obtained amplitude values of the power spectral density values in the third preset frequency range corresponding to each single voltage or each single current, the obtained amplitude values of all the power spectral density values can be compared two by two to obtain the largest amplitude value and the smallest amplitude value in the amplitude values of all the power spectral density values, and the difference value between the largest amplitude value and the smallest amplitude value is determined as the variation between the amplitude values of the power spectral density values in the third preset frequency range corresponding to a plurality of different voltages or a plurality of different currents, so that the noise evaluation value of the target state index is obtained.

The process of determining the noise evaluation value of the target state index based on the amount of change between the turning frequencies of the plurality of power spectral density frequency curves will be described below.

Based on the power spectral density frequency curves corresponding to the different voltages or the different currents obtained in the steps, for each power spectral density frequency curve, the turning frequency of the power spectral density frequency curve can be obtained according to the power spectral density frequency curve. The method for obtaining the turning frequency of the power spectrum density frequency curve according to the power spectrum density frequency curve is similar to the method for obtaining the turning frequency of the power spectrum density frequency curve according to the power spectrum density frequency curve, and is not repeated here.

Further, based on the acquired turning frequencies of the power spectrum density frequency curves corresponding to each single voltage or each single current, the turning frequencies of all the acquired power spectrum density frequency curves can be compared to obtain the maximum turning frequency and the minimum turning frequency in the turning frequencies of all the power spectrum density frequency curves, and the difference between the maximum turning frequency and the minimum turning frequency is determined as the variation between the turning frequencies of a plurality of different voltages or a plurality of different currents corresponding to a plurality of power spectrum density frequency curves, so that the noise evaluation value of the target state index is obtained.

The process of determining the noise evaluation value of the target state index according to the amount of change between slopes in the fourth preset frequency range in the plurality of power spectral density frequency curves will be described below.

Based on the power spectral density frequency curves corresponding to the different voltages or the different currents obtained in the steps, for each power spectral density frequency curve, the slope of the power spectral density frequency curve in the fourth preset frequency range can be obtained according to the power spectral density frequency curve. The method for obtaining the slope of the power spectral density frequency curve within the fourth preset frequency range according to the power spectral density frequency curve is similar to the method for obtaining the slope of the power spectral density frequency curve within the second preset frequency range according to the power spectral density frequency curve in the previous embodiment, and will not be described in detail in this embodiment of the present application. Optionally, the fourth preset frequency range in the present embodiment may be equal to or different from the second preset frequency range in the previous embodiment, which is not limited in any way.

Further, based on the obtained slopes in the fourth preset frequency range corresponding to each single voltage or each single current, comparison processing can be performed on all the obtained slopes in the fourth preset frequency range to obtain a maximum slope and a minimum slope in all the slopes in the fourth preset frequency range, and the difference between the maximum slope and the minimum slope is determined as the variation between the slopes in the fourth preset frequency range corresponding to a plurality of different voltages or a plurality of different currents, so that the noise evaluation value of the target state index is obtained.

According to the battery state detection method, the noise evaluation value of the target state index can be determined in a plurality of modes according to the acquired power spectrum density frequency curves of the battery under a plurality of different voltages or a plurality of different currents, so that the noise evaluation value of the target state index can be determined in an optimal mode according to actual application requirements, the speed and efficiency of determining the noise evaluation value of the target state index are improved, the determination process of the noise evaluation value is simplified, the noise evaluation value of the target state index can be determined through the power spectrum density frequency curves of the battery under a single voltage or a single current, the noise evaluation value of the target state index can be determined through the power spectrum density frequency curves of the battery under a plurality of different voltages or a plurality of different currents, the diversity of the determination modes of the noise evaluation value of the target state index is increased, and the mode of determining the noise evaluation value of the target state index is more flexible.

The procedure of acquiring the noise evaluation value of the target state index from the low-frequency noise test data in the above steps will be described below. In an embodiment, the step in S210 may include determining the low frequency noise test data as the noise evaluation value of the target state index, or determining the noise evaluation value of the target state index according to the low frequency noise test data of the battery and the low frequency noise test data of other batteries in the module where the battery is located.

In the embodiment of the present application, in order to reduce the operation amount in the determination process of the noise evaluation value, the low-frequency noise test data may be directly determined as the noise evaluation value of the target state index.

However, the low-frequency noise test data is directly determined as the noise evaluation value of the target state index, which may cause a problem that the battery state detection result is not very accurate.

The low-frequency noise test data of the battery and the low-frequency noise test data of other batteries can be compared in pairs to obtain the maximum low-frequency noise test data or the minimum low-frequency noise test data in the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries, and the maximum low-frequency noise test data or the minimum low-frequency noise test data is determined to be a noise evaluation value of the target state index.

Or the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries may be weighted and summed, and the weighted and summed result is determined as the noise evaluation value of the target state index.

According to the battery state detection method provided by the embodiment of the application, the noise evaluation value of the target state index can be determined in different modes according to actual demands, so that the process of determining the noise evaluation value of the target state index is flexible.

In practical application, if the calculation amount in the battery state detection process needs to be reduced to a great extent, the adjacent noise correlation coefficient method may be used to determine the noise evaluation value of the target state index, and the process of determining the noise evaluation value of the target state index by using the adjacent noise correlation coefficient method is described below. In an embodiment, as shown in fig. 8, the step of determining the noise evaluation value of the target state index according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located may include:

s213, determining the noise correlation coefficient between the battery and the adjacent battery of the battery according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries. Wherein the noise correlation coefficient represents the degree of noise deviation of the battery from the neighboring battery.

Specifically, the noise correlation coefficient between the battery and the adjacent battery of the battery is obtained according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries. The low-frequency noise test data of the battery and the adjacent battery of the battery can be used as a quotient to obtain the noise correlation coefficient between the battery and the adjacent battery of the battery.

However, in the embodiment of the application, the low-frequency noise test data between the battery and the adjacent battery of the battery is subjected to difference to obtain the noise correlation coefficient between the battery and the adjacent battery of the battery.

S214, determining the noise correlation coefficient as a noise evaluation value of the target state index.

Wherein the noise correlation coefficient between the battery and the adjacent battery of the battery is directly determined as the noise evaluation value of the target state index. Alternatively, the noise correlation coefficient may be equal to any value within the [0,1] interval.

According to the battery state detection method, the noise correlation coefficient between the battery and the adjacent battery of the battery can be determined according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries, and the noise correlation coefficient is determined to be the noise evaluation value of the target state index, so that the method can determine the noise evaluation value of the target state index in an average value mode, can determine the noise evaluation value of the target state index in a mode of calculating the noise correlation coefficient between the battery and the adjacent battery of the battery, the determination mode of the noise evaluation value of the target state index is further increased, the mode of determining the noise evaluation value of the target state index is more flexible, meanwhile, the method does not need to consider the test data of other batteries in a module when determining the noise evaluation value of the target state index, and accuracy of the noise evaluation value can be improved.

In order to improve the accuracy of the detection result of the battery under the target state index, a noise evaluation value corresponding to the battery under the target critical state can be acquired first, and then the detection result of the battery under the target state index can be determined according to the noise evaluation value and the noise critical condition of the battery under the target state index. In an embodiment, as shown in fig. 9, the step of determining the detection result of the battery under the target state index according to the noise evaluation value and the detection policy of the target state index in S220 may include:

s221, obtaining a noise critical condition corresponding to the battery in the target critical state.

Specifically, the target critical state may be a critical state corresponding to a manufacturing stage of the battery or a critical state corresponding to a use stage of the battery. Here, if the target state index is a manufacturing defect (e.g., voltage drop of the battery per unit time, i.e., K value), the target critical state may be understood as a state in which the normal battery is just a defective battery in the manufacturing stage, if the target state index is a self-discharge degree, the target critical state may be understood as a state in which the normal battery is just a battery with a high self-discharge degree in the use stage, if the target state index is an aging degree, the target critical state may be understood as a state in which the normal battery is just a battery with a large aging degree in the use stage, if the target state index is a lithium precipitation degree, the target critical state may be understood as a state in which the normal battery is just a battery with a large lithium precipitation degree in the use stage, and if the target state index is an internal short circuit degree, the target critical state may be understood as a state in which the normal battery is just a battery with a large internal short circuit degree in the use stage. It will also be appreciated herein that the target state index is in one-to-one correspondence with the target critical state.

The above-mentioned noise critical condition of the battery in the target critical state may be understood as obtaining the noise evaluation value of the battery in the target critical state. In practical application, a plurality of experiments can be used for obtaining the corresponding noise critical condition of the battery in the target critical state. For example, a large number of normal batteries of the corresponding system may be tested under a certain noise test condition to obtain noise evaluation values of the normal batteries, and the noise evaluation values of the normal batteries may be determined as noise critical conditions of the batteries of the corresponding system in a target critical state. For example, the noise critical condition of the normal battery of the corresponding system obtained by the historical experiment can be subjected to equation fitting, the unknown parameter or the unknown parameter range in the fitting equation is determined by an estimation algorithm, and then the noise critical condition of the battery in the target critical state is determined according to the unknown parameter or the unknown parameter range. Alternatively, the noise critical condition may be a noise critical threshold, and may also be a noise critical threshold range (i.e., including an upper noise critical threshold and a lower noise critical threshold). The noise threshold conditions corresponding to different threshold states may be the same or different.

The method for acquiring the noise critical condition corresponding to the battery in the target critical state is the same as the method for acquiring the noise evaluation value of the battery in the target state index, and is not repeated in the embodiment of the present application.

S222, determining a detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition.

And determining a detection result of the battery under the target state index based on the noise evaluation value of the battery under the target state index obtained in the previous step and the corresponding noise critical condition of the battery under the target critical state.

The method for determining the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition may be to input the noise evaluation value of the battery under the target state index obtained in the previous step and the noise critical condition corresponding to the battery under the target critical state into the algorithm model through a pre-trained algorithm model, so as to obtain the detection result of the battery under the target state index.

In addition, the method of determining the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition may also be to compare the noise evaluation value and the noise critical condition and process the comparison result to obtain the detection result of the battery under the target state index. Alternatively, the comparison result may be that the noise evaluation value does not meet the noise critical condition or the noise evaluation value meets the noise critical condition, and the detection result of the battery under the target state index may be abnormal or normal.

In addition, in practical applications, in other situations, the accurate detection result may be determined through the noise evaluation value and the noise critical condition, and based on this, in one embodiment, the step of determining the detection result of the battery under the target state index in S222 according to the noise evaluation value and the noise critical condition may include determining that the detection result of the battery under the target state index is abnormal if the noise evaluation value does not meet the noise critical condition, and determining that the detection result of the battery under the target state index is normal if the noise evaluation value meets the noise critical condition.

In the embodiment of the application, if the noise evaluation value is determined to not meet the noise critical condition, the detection result of the battery under the target state index can be directly determined to be abnormal, and if the noise evaluation value is determined to meet the noise critical condition, the detection result of the battery under the target state index can be directly determined to be normal.

It should be noted that, if the target state index is the manufacturing defect level, the detection result of the battery under the target state index is abnormal and can be understood as that the battery has the manufacturing defect, if the target state index is the self-discharge level, the detection result of the battery under the target state index is abnormal and can be understood as that the battery has the defect of high self-discharge level, if the target state index is the aging level, the detection result of the battery under the target state index is abnormal and can be understood as that the battery has the defect of high aging level, if the target state index is the lithium precipitation level, the detection result of the battery under the target state index is abnormal and can be understood as that the battery has the defect of high lithium precipitation level, and if the target state index is the internal short circuit level, the detection result of the battery under the target state index is abnormal and can be understood as that the battery has the defect of high internal short circuit level. The detection result of the battery under the target state index is normal, which can be understood that the battery has no defect and is a battery under a normal state.

According to the battery state detection method provided by the embodiment of the application, the corresponding noise critical condition of the battery in the target critical state can be obtained, and the detection result of the battery in the target state index is determined according to the noise evaluation value and the noise critical condition of the battery in the ideal state, so that the determined detection result is more accurate, and the accuracy of the detection result of the battery in the target state index is improved.

In practical applications, the noise critical condition is obtained by the target critical state battery, but the standard and reliable target critical state battery is difficult to produce, so that the noise critical condition can be determined by adopting a simple, convenient and quick average noise method, and the process of determining the noise critical condition by adopting the average noise method is described below. In an embodiment, the noise critical condition is a noise threshold, as shown in fig. 10, the battery state detection method may further include:

S223, obtaining a low-frequency noise test average value between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located.

In the embodiment of the application, the low-frequency noise test data of other batteries in the module where the batteries are located are also required to be acquired. The method for acquiring the low-frequency noise test data of the other batteries in the module where the battery is located is similar to the method for acquiring the low-frequency noise test data of the battery in the step S100, and thus the embodiment of the application is not repeated.

Based on the obtained low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located, the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries can be subjected to average processing to obtain a low-frequency noise test average value between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries.

S224, determining the low-frequency noise test average value as a noise threshold value.

In the embodiment of the application, the obtained low-frequency noise test average value is directly determined as a noise critical condition, namely a noise threshold value.

The battery state detection method in the embodiment of the application can also acquire the low-frequency noise test data of other batteries in the module where the battery is located on the basis of acquiring the low-frequency noise test data of the battery, acquire the low-frequency noise test average value between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries, and then determine the low-frequency noise test average value as a noise threshold value.

In some scenarios, the level of the abnormal detection result needs to be classified to improve the fineness of the abnormal detection result, and provide a basis for selecting effective solving measures when solving the abnormality, based on which a plurality of different noise critical conditions need to be set to determine detection results of different degrees of the battery under the target state index. In an embodiment, if the noise critical condition includes a first threshold, a second threshold, and a third threshold, as shown in fig. 11, the step of determining the detection result of the battery under the target state index according to the detection policy of the noise evaluation value and the target state index in S220 may include:

S225, if the noise evaluation value exceeds the first threshold and does not exceed the second threshold, determining that the detection result of the battery under the target state index is suspected abnormal.

In order to rank the abnormality detection result, the noise critical condition is a noise critical threshold, and a plurality of noise critical thresholds of different ranks may be included. In the embodiment of the application, taking an example that the noise critical condition includes three noise critical thresholds with different levels as the example, the abnormal detection results with different degrees of the battery under the target state index are determined, wherein the noise critical condition may include a first threshold, a second threshold and a third threshold. Here, the first threshold value indicates a noise threshold value at which the abnormality level of the battery is relatively low, the second threshold value indicates that the abnormality level of the battery belongs to a medium noise threshold value, and the third threshold value indicates a noise threshold value at which the abnormality level of the battery is relatively high. In an embodiment of the application, the first threshold is smaller than the second threshold and smaller than the third threshold.

After the noise critical threshold of the battery in the target critical state is obtained, the noise critical threshold can be reduced to obtain a first threshold, the noise critical threshold is determined to be a second threshold, the noise critical threshold is enlarged to obtain a third threshold, and the noise critical threshold can be adjusted according to a preset adjustment rule to obtain the first threshold, the second threshold and the third threshold respectively.

Specifically, it may be determined whether the noise evaluation value of the target state indicator is greater than the first threshold and less than the second threshold, and if it is determined that the noise evaluation value of the target state indicator is greater than the first threshold (i.e., the noise evaluation value exceeds the first threshold), and if the noise evaluation value of the target state indicator is less than the second threshold (i.e., the noise evaluation value does not exceed the second threshold), it is determined that the detection result of the battery under the target state indicator is suspected to be abnormal.

And S226, if the noise evaluation value exceeds the second threshold value and does not exceed the third threshold value, determining that the detection result of the battery under the target state index is slightly abnormal.

Meanwhile, it may be determined whether the noise evaluation value of the target state index is greater than the second threshold and less than the third threshold, and if it is determined that the noise evaluation value of the target state index is greater than the second threshold (i.e., the noise evaluation value exceeds the second threshold), and the noise evaluation value of the target state index is less than the third threshold (i.e., the noise evaluation value exceeds the third threshold), it is determined that the detection result of the battery under the target state index is slightly abnormal.

And S227, if the noise evaluation value exceeds a third threshold value, determining that the detection result of the battery under the target state index is serious abnormality.

And, whether the noise evaluation value of the target state index is greater than the third threshold value can be judged, if the noise evaluation value of the target state index is greater than the third threshold value (i.e. the noise evaluation value exceeds the third threshold value), the detection result of the battery under the target state index is determined to be seriously abnormal.

In the embodiment of the application, three different degrees of abnormality detection results, namely suspected abnormality, slight abnormality and serious abnormality, can be determined according to the first threshold, the second threshold and the third threshold, but the processing process is not limited to determining three different degrees of abnormality detection results through three different grade thresholds, and in order to improve the accuracy of the determined abnormality detection results, a plurality of different grade thresholds can be set to determine a plurality of different degrees of abnormality detection results.

In addition, in the embodiment of the present application, if the target state index is the aging degree, the computer device may further directly determine the low-frequency noise test data of the battery as the noise evaluation value of the target state index, and then compare the noise evaluation value with the first threshold, the second threshold and the third threshold respectively, so as to obtain a detection result of the battery under the aging degree. Alternatively, the first threshold value, the second threshold value, and the third threshold value may be noise evaluation values of different aging degrees corresponding to the battery, respectively.

A process of determining a detection result of the battery at the aging degree based on the noise evaluation value and the plurality of noise threshold values corresponding to the aging degree will be described below by way of example. For example, under a certain voltage, the batteries with different aging degrees can be subjected to low-frequency noise tests in advance, for example, the batteries with the aging degrees of 100%, 90%, 85%, 80%, 75% and 70% respectively, so as to obtain low-frequency noise test data of the batteries with different aging degrees, based on the low-frequency noise test data, when determining the detection result of each battery in a module under the aging degree, the aging degree of each battery to be tested in the module can be determined first, and aiming at each battery to be tested, carrying out low-frequency noise test on the battery to be tested, if the obtained low-frequency noise test data are between the low-frequency noise test data corresponding to the battery with the predetermined aging degree of 85% and 80%, the aging degree of the battery to be tested is between 85% and 80%, and further determining the detection result of the battery under the aging degree according to the aging degree interval of the battery to be tested. Here, the low-frequency noise test data of the battery to be tested is a noise evaluation value, and the low-frequency noise test data corresponding to 85% and 80% of the batteries may be two noise thresholds corresponding to the degree of aging.

Since the above example needs to perform the low-frequency noise test on the batteries with different aging degrees in advance to obtain the judgment basis of the aging degrees of the different batteries, but the same battery produced in different batches may have a difference, and the battery may amplify the difference in the aging process, the judgment basis obtained by testing the batteries in one batch cannot be applied to the batteries in all batches, otherwise the detection accuracy may be seriously reduced. Meanwhile, if only a few batteries with different aging degrees are selected, for example, the aging degrees are 100%, 90%, 85%, 80%, 75% and 70% respectively, so that the estimated deviation of the aging degree is necessarily greater than or equal to 5%, and the detection precision in practical application may not meet the practical requirements. If a large number of batteries with relatively close aging degrees are selected for low-frequency noise test to determine the judgment basis of the aging degrees of different batteries, the cost is relatively high, the process is relatively complex, and the problem of accuracy of state detection results of batteries in different production batches still cannot be solved. In addition, in practical application, the specific aging degree of each battery is not required to be determined, and only abnormal batteries with the aging degree obviously different from other batteries need to be detected. Since the above problems may exist in determining the detection result of the battery under the aging degree using the above example, in order to solve the problems, the embodiment of the present application may also determine the detection result of the battery under the aging degree in the following manner.

The method comprises the steps of obtaining low-frequency noise test data of a battery to be tested, obtaining low-frequency noise test data of the battery to be tested, obtaining low-frequency noise test average values between the low-frequency noise test data of the battery to be tested and low-frequency noise test data of other batteries in a module where the battery to be tested is located, determining the low-frequency noise test average values as noise threshold values, comparing the low-frequency noise test data of each battery to be tested with the noise threshold values, and determining that the aging degree of the battery to be tested is higher when the difference value between the low-frequency noise test data and the noise threshold values is obviously larger than the difference value between the low-frequency noise test data of other batteries and the noise threshold values, and further determining the detection result of the battery under the aging degree according to the approximate aging degree of the battery to be tested.

Since the above example requires calculation of the low frequency noise test average value of all the batteries in the module to determine the noise threshold, the calculation amount is large, and thus the low frequency noise test data of the abnormal battery is also included in the calculated noise threshold, thereby introducing unnecessary errors. To solve this problem, the embodiment of the present application may also determine the detection result of the battery under the degree of aging in the following manner.

For example, the noise correlation coefficient of the battery to be measured may be calculated, and compared with a preset noise threshold, if the noise correlation coefficient is greater than the noise threshold, the corresponding noise correlation coefficient is greater (i.e., the noise evaluation value is greater), and the noise correlation coefficient is closer to 1, which represents that a battery with higher aging degree will necessarily exist in the battery to be measured and the adjacent battery of the battery to be measured, further, in order to determine the battery with higher aging degree in the battery to be measured and the adjacent battery of the battery to be measured, the noise correlation coefficients between the battery to be measured and the adjacent battery corresponding thereto may be continuously calculated, and the battery with higher aging degree in the battery to be measured and the adjacent battery of the battery to be measured may be located based on the calculated three noise correlation coefficients.

In the embodiment of the present application, the step S225, the step S226 and the step S227 may be performed synchronously or asynchronously, and the step S225, the step S226 and the step S227 may be performed in any order during the asynchronous execution, which is not limited. Fig. 11 is a schematic diagram of the sequence of steps S225, S226, and S227.

In practical applications, it may also be determined whether the noise evaluation value is greater than the first threshold, and if the noise evaluation value is greater than the first threshold, the current battery is determined to be an abnormal battery, and then the steps in step S223, step S226, and step S227 are performed continuously.

According to the battery state detection method, the abnormal detection results of the battery at different degrees under the target state indexes can be determined by setting a plurality of different level thresholds, so that the determined level of the abnormal detection result is finer, and accurate basis is provided for effective solving measures to be selected when the abnormality is solved.

In practical applications, after determining that the detection result of the battery under the target state index is abnormal, the abnormality needs to be solved. Based on this, in one embodiment, after the steps are performed, as shown in fig. 12, the battery state detection method further includes the steps of:

S300, acquiring a back-end processing strategy corresponding to the target state index.

The back-end processing policy may be a processing policy that is pre-planned according to a target state index, where the back-end processing policy may be stored in a local, cloud, or hard disk. The back-end processing strategy is a processing method for solving the problem of abnormality of the target state index of the battery. Alternatively, the abnormality of the target state index may be understood as an abnormality of the detection result of the battery under the target state index.

In practical application, the back-end processing strategy corresponding to the target state index can be obtained from a local position, a cloud end position or a hard disk position and the like. Optionally, the back-end processing strategies corresponding to different state indexes may be the same or different. In the embodiment of the present application, the back-end processing policy may also be understood as a battery management policy, i.e., (BatteryManagement System, BMS) power battery management system policy.

And S400, if the detection result of the battery under the target state index is abnormal, processing the battery according to a back-end processing strategy corresponding to the target state index.

When the detection result of the battery under the target state index is abnormal, the battery can be processed based on the acquired back-end processing strategy corresponding to the target state index. Alternatively, the back-end processing policies may include repair style, replacement information, and the like.

After the detection result is determined, the abnormal batteries with different degrees are mainly processed correspondingly so as to improve the safety of the batteries, and the process of processing the batteries according to the back-end processing strategy corresponding to the target state index is described below. In an embodiment, the step of processing the battery according to the back-end processing policy corresponding to the target state index in S400 may include determining a detection result of the battery under the target state index by a secondary confirmation method if the detection result of the battery under the target state index is the suspected abnormality, repairing the battery by a repairing operation if the detection result of the battery under the target state index is the slight abnormality, and scrapping the battery by a scrapping operation if the detection result of the battery under the target state index is the serious abnormality.

If the detection result of the battery under the target state index is determined to be suspected abnormal, the battery can be reconfirmed in a secondary confirmation mode, so that a more accurate detection result is obtained. Meanwhile, if the detection result of the battery under the target state index is determined to be slightly abnormal, the battery can be repaired through repair operation, and if the detection result of the battery under the target state index is determined to be severely abnormal, the battery can be scrapped through scrapping operation.

In the embodiment of the present application, the back-end processing policies corresponding to the anomaly detection results with different degrees are not limited to the back-end processing policies described above, but may be other back-end processing policies. The method includes the steps of determining that a detection result is slightly abnormal, namely the internal short-circuit degree is low when a target state index is the internal short-circuit degree and a noise evaluation value exceeds a first threshold and does not exceed a second threshold, at the moment, reconfirming the battery in a secondary confirmation mode, determining that the detection result is moderately abnormal, namely the internal short-circuit degree is high when the noise evaluation value exceeds the second threshold and does not exceed a third threshold, at the moment, replacing the battery or the whole module where the battery is located, determining that the detection result is highly abnormal, namely the internal short-circuit degree is very high when the noise evaluation value exceeds the third threshold, at the moment, outputting reminding information to inform a user to keep away from the battery and take protective and fire-fighting measures.

If the target state index is the lithium precipitation degree, and the noise evaluation value exceeds a first threshold and does not exceed a second threshold, the detection result can be determined to be slightly abnormal, namely the lithium precipitation degree is low, at the moment, the battery can be confirmed again in a secondary confirmation mode, if the noise evaluation value exceeds the second threshold and does not exceed a third threshold, the detection result is determined to be moderately abnormal, namely the lithium precipitation degree is high, at the moment, reminding information can be output to inform a user that the battery has safety risk and needs to replace the battery or the whole module where the battery is located, if the noise evaluation value exceeds the third threshold, the detection result is determined to be highly abnormal, namely the lithium precipitation degree is very high, at the moment, reminding information can be output to inform the user that the battery has high risk of causing safety accidents due to internal short circuit caused by dendrite puncturing a diaphragm, and the user is away from the battery and takes protection and fire-fighting measures.

The embodiment of the application can correspondingly process the abnormal batteries with different degrees by adopting different back-end processing strategies so as to prevent the occurrence of safety accidents in time and increase the use experience of users.

According to the method, when the detection result is determined to be suspected abnormal, the detection result of the battery under the target state index can be confirmed again, so that the accuracy of the detection result is ensured, the problem that the normal battery is mishandled by adopting a back-end processing strategy can be avoided, resources are saved, the problem that the safety of the battery is lower because the abnormal battery is not timely handled by adopting the back-end processing strategy can be avoided, meanwhile, the battery can be correspondingly handled by adopting the corresponding back-end processing strategy according to the abnormal detection result of different grades, the poor battery can be prevented from flowing into the market to cause safety accidents, the abnormal battery can be prevented from being normally used in the using stage to cause customer complaints, the safety of the battery is further improved, and the using experience of a user is improved.

The procedure for acquiring the low-frequency noise test data of the battery described above will be described below. In one embodiment, the step in S100 may be implemented by obtaining, through a low-frequency noise test system built in advance, voltage or current fluctuation data of the battery in a specific current discharging process to obtain low-frequency noise test data, or detecting, through an integrated test chip, voltage or current fluctuation data of the battery in time to obtain low-frequency noise test data.

In the embodiment of the application, two different detection modes can be adopted to detect the fluctuation data of the voltage or current with time in the discharging process of the battery with specific current, namely the low-frequency noise test data of the battery, and the fluctuation data obtained in the two modes are the same.

The low-frequency noise test system is shown in fig. 1, and comprises an adjustable resistor, low-frequency noise test equipment and an amplifier, wherein the connection relation among the adjustable resistor, the low-frequency noise test equipment and the amplifier is shown in fig. 1, the adjustable resistor is connected with the amplifier in parallel, the amplifier is connected with the low-frequency noise test equipment in parallel, and in the test process, the two ends of the adjustable resistor are respectively connected with the anode and the cathode of a battery. The low-frequency noise test system is used for fixing different discharge voltages or different discharge currents of the battery in the low-frequency noise test process by adjusting the resistance value of the adjustable resistor, amplifying the discharge voltage or the discharge current output by the battery through the amplifier, and detecting fluctuation data of the amplified discharge voltage or the discharge current along with time through the low-frequency noise test equipment to obtain low-frequency noise test data of the battery. Further, the low frequency noise test device may transmit the detected low frequency noise test data of the battery to the computer device.

In the embodiment of the application, the fluctuation data of the voltage or the current of the battery along with time can be detected through the integrated test chip, so that the low-frequency noise test data of the battery can be obtained, and further, the integrated test chip can send the detected low-frequency noise test data of the battery to the computer equipment. The detection effect of the integrated test chip and the detection effect of the low-frequency noise test system are the same, and only the structure in the low-frequency noise test system is integrated on the test chip, so that the integrated test chip is smaller in size and can achieve the effect of convenient detection.

In some scenarios, it is necessary to detect the battery status of all batteries in one module, so the initial parameter settings of each battery in the module need to be consistent, so that the detected battery status of all batteries in the same module is more accurate. Based on this, in one embodiment, before executing the step in S100, the method for detecting a battery state may further include a step of detecting whether the voltage of the battery meets the voltage consistency requirement, a step of acquiring low-frequency noise test data of the battery if the voltage meets the voltage consistency requirement, and a step of adjusting the battery to meet the voltage consistency requirement through a charging and discharging device if the voltage does not meet the voltage consistency requirement.

When the battery state detection is performed on all the batteries in the same module, the electric quantity of each battery in the same module can be set to be the same electric quantity, but in the embodiment of the application, the voltage of each battery in the same module can be set to be the same voltage in order to shorten the duration of the detection process due to the slower charging speed of the battery, so that the detection process can be rapidly completed.

Specifically, the computer device can detect the voltages of the batteries in the same module by controlling the voltage detector, then compare whether the voltages of the batteries in the same module are consistent or not to determine whether the voltages of the batteries in the same module meet the voltage consistency requirement, if the voltages of the batteries in the same module are equal, determine that the voltages of the batteries in the same module meet the voltage consistency requirement, and if the voltages of the batteries in the same module are not equal, determine that the voltages of the batteries in the same module do not meet the voltage consistency requirement, and at the moment, control the charging and discharging device to regulate the voltages of the batteries in the same module to be equal so that the batteries in the module meet the state of the voltage consistency requirement.

The charging and discharging equipment is corresponding to the type of the battery in the module. The charging and discharging device may be a lithium battery charging and discharging device, such as a square lithium battery charging and discharging device, a soft package lithium battery charging and discharging device, a cylindrical lithium battery charging and discharging device, or the like, if the type of the battery in the module is a lithium battery, and a lead-acid battery charging and discharging device, such as a silicon controlled rectifier charging and discharging device, a bus type grid-connected charging and discharging device, or the like, if the type of the battery in the module is a lead-acid battery.

The battery state detection method in the embodiment of the application can acquire the low-frequency noise test data of the battery in various modes, thereby improving the flexibility of acquiring the low-frequency noise test data of the battery.

In one embodiment, the present application also provides a battery state detection method, including the steps of:

(1) And detecting whether the voltage of the battery meets the voltage consistency requirement.

(2) And if the low-frequency noise test data of the battery are not met, regulating the battery to meet the voltage consistency requirement through the charging and discharging equipment.

(3) And acquiring the voltage or current fluctuation data of the battery in the discharging process of the battery with the specific current through a pre-built low-frequency noise test system to obtain low-frequency noise test data, or detecting the voltage or current fluctuation data of the battery with time through an integrated test chip to obtain the low-frequency noise test data.

(4) And acquiring a noise evaluation value of the target state index according to the low-frequency noise test data, wherein the noise evaluation value represents the noise level of the healthy state of the battery under the target state index.

The process in step (4) may be implemented in three ways:

First, the process in step (4) includes the steps of:

(41) Performing frequency domain conversion processing on the low-frequency noise test data to obtain noise power spectrum data of the battery;

(42) A noise evaluation value of the target state index is determined from the noise power spectrum data of the battery.

Wherein the process in step (42) can be implemented in two ways:

first, the noise power spectrum data comprises a power spectrum density frequency curve at a single voltage or a single current, and then the process in step (42) comprises at least one of:

(421) According to the power spectral density frequency curve, determining a power spectral density value at a specified frequency as a noise evaluation value of a target state index;

(422) According to the power spectral density frequency curve, determining the amplitude of a power spectral density value in a first preset frequency range as a noise evaluation value of a target state index;

(423) Determining the turning frequency of the power spectrum density frequency curve as a noise evaluation value of a target state index;

(424) And determining the slope of the power spectral density frequency curve in a second preset frequency range as a noise evaluation value of the target state index.

Second, the noise power spectrum data comprises a plurality of power spectrum density frequency curves at a plurality of different voltages or a plurality of different currents, and the process in step (42) comprises at least one of:

(425) According to the variation among the power spectral density values at the designated frequency in the power spectral density frequency curves, determining a noise evaluation value of the target state index;

(426) According to the variation among the magnitudes of the power spectral density values in the third preset frequency range in the power spectral density frequency curves, determining a noise evaluation value of the target state index;

(427) Determining a noise evaluation value of the target state index according to the variation among turning frequencies of the power spectrum density frequency curves;

(428) And determining a noise evaluation value of the target state index according to the change amount between slopes in a fourth preset frequency range in the power spectrum density frequency curves.

Second, the process in step (4) includes the steps of:

(43) The low-frequency noise test data is determined as a noise evaluation value of the target state index.

Third, the process in step (4) includes the steps of:

(45) Determining a noise correlation coefficient between the battery and adjacent batteries of the battery according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries;

(46) And determining the noise correlation coefficient as a noise evaluation value of the target state index.

(5) And if the noise critical condition is a noise threshold value, acquiring a low-frequency noise test average value between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located, and determining the low-frequency noise test average value as the noise threshold value.

(6) The noise critical condition comprises a first threshold value, a second threshold value and a third threshold value, if the noise evaluation value exceeds the first threshold value and does not exceed the second threshold value, the detection result of the battery under the target state index is determined to be suspected abnormal, if the noise evaluation value exceeds the second threshold value and does not exceed the third threshold value, the detection result of the battery under the target state index is determined to be slightly abnormal, if the noise evaluation value exceeds the third threshold value, the detection result of the battery under the target state index is determined to be serious abnormal, or if the noise evaluation value is smaller than the noise threshold value, the detection result of the battery under the target state index is determined to be abnormal, and if the noise evaluation value is smaller than the noise threshold value, the detection result of the battery under the target state index is determined to be normal.

(7) And acquiring a back-end processing strategy corresponding to the target state index.

(8) If the detection result of the battery under the target state index is suspected to be abnormal, determining the detection result of the battery under the target state index in a secondary confirmation mode, repairing the battery through repairing operation if the detection result of the battery under the target state index is slightly abnormal, and scrapping the battery through scrapping operation if the detection result of the battery under the target state index is severely abnormal.

The implementation process of the above (1) to (8) can be specifically referred to the description of the above embodiment, and its implementation principle and technical effects are similar, and will not be described herein again.

It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a battery state detection device for realizing the above related battery state detection method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation of one or more embodiments of the battery state detection device provided below may be referred to the limitation of the battery state detection method hereinabove, and will not be repeated herein.

In one embodiment, fig. 13 is a schematic structural diagram of a battery state detection device according to an embodiment of the present application, where the battery state detection device provided in the embodiment of the present application may be applied to a computer device. As shown in fig. 13, the battery state detection apparatus of the embodiment of the present application may include a test data acquisition module 11 and a detection result determination module 12.

The test data acquisition module 11 is used for acquiring low-frequency noise test data of the battery;

the detection result determining module 12 is configured to determine a detection result of the battery under the target state index according to the low-frequency noise test data and a detection policy of the target state index, where the target state index includes a plurality of different battery state indexes.

The battery state detection device provided by the embodiment of the application can be used for executing the technical scheme in the embodiment of the battery state detection method, and the implementation principle and the technical effect are similar, and are not repeated here.

In one embodiment, the detection result determining module 12 includes a noise evaluation value acquiring unit and a detection result determining unit, wherein:

The system comprises a noise evaluation value acquisition unit, a noise evaluation value generation unit and a noise evaluation value generation unit, wherein the noise evaluation value acquisition unit is used for acquiring a noise evaluation value of a target state index according to low-frequency noise test data;

and the detection result determining unit is used for determining the detection result of the battery under the target state index according to the noise evaluation value and the detection strategy of the target state index.

In one embodiment, the noise evaluation value acquisition unit includes a frequency domain conversion processing subunit and a first determination subunit, wherein:

the frequency domain conversion processing subunit is used for carrying out frequency domain conversion processing on the low-frequency noise test data to obtain noise power spectrum data of the battery;

a first determination subunit for determining a noise evaluation value of the target state index according to the noise power spectrum data of the battery.

In one embodiment, the noise power spectral data comprises a power spectral density frequency curve at a single voltage or a single current, the first determining subunit being configured to:

In an embodiment the noise power spectral data comprises a plurality of power spectral density frequency curves at a plurality of different voltages or a plurality of different currents, the first determining subunit being further adapted to:

In one embodiment, the noise evaluation value acquisition unit includes a first determination subunit or a second determination subunit, wherein:

A first determination subunit configured to determine the low-frequency noise test data as a noise evaluation value of the target state index;

And the second determination subunit is used for determining the noise evaluation value of the target state index according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located.

In one embodiment, the second determining subunit is specifically configured to:

In one embodiment, the detection result determining unit includes a critical condition acquiring subunit and a detection result determining subunit, wherein:

The critical condition acquisition subunit is used for acquiring the noise critical condition corresponding to the battery under the target state index;

and the detection result determining subunit is used for determining the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition.

In one embodiment, the detection result determining subunit is specifically configured to:

In one embodiment, the noise critical condition is a noise threshold, and the detection result determining unit further includes an average value obtaining subunit and a third determining subunit, wherein:

the average value obtaining subunit is used for obtaining the low-frequency noise test average value between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries;

and the third determining subunit is used for determining the low-frequency noise test average value as a noise threshold value.

In one embodiment, the noise critical condition comprises a first threshold, a second threshold and a third threshold, and the detection result determining subunit is further specifically configured to:

if the noise evaluation value exceeds the third threshold value, determining that the detection result of the battery under the target state index is serious abnormality.

In one embodiment, the battery state detection device further comprises a processing strategy acquisition module and an exception processing module, wherein:

The processing strategy acquisition module is used for acquiring a back-end processing strategy corresponding to the target state index;

And the abnormality processing module is used for processing the battery according to the back-end processing strategy corresponding to the target state index when the detection result of the battery under the target state index is abnormal.

In one embodiment, the exception includes suspected exception, slight exception and serious exception, and the exception handling module includes a first handling unit, a second handling unit and a third handling unit, wherein:

The first processing unit is used for determining the detection result of the battery under the target state index in a secondary confirmation mode when the detection result of the battery under the target state index is suspected to be abnormal;

the second processing unit is used for repairing the battery through repairing operation when the detection result of the battery under the target state index is slightly abnormal;

and the third processing unit is used for scrapping the battery through scrapping operation when the detection result of the battery under the target state index is serious abnormality.

In one embodiment, the test data acquisition module 11 is specifically configured to:

In one embodiment, the battery state detection device further comprises a consistency detection module, a first determination module and a second determination module, wherein:

The consistency detection module is used for detecting whether the voltage of the battery meets the voltage consistency requirement;

the first determining module is used for executing the step of acquiring the low-frequency noise test data of the battery when the detection result of the consistency detecting module is satisfied;

and the second determining module is used for adjusting the battery to meet the voltage consistency requirement through the charging and discharging equipment when the detection result of the consistency detecting module is not met.

For specific limitations of the battery state detection device, reference may be made to the above limitations of the battery state detection method, and no further description is given here. The respective modules in the above-described battery state detection device may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 14. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and an information base. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The information base of the computer device is used for storing driving characteristic information. The network interface of the computer device is for communicating with an external endpoint via a network connection. The computer program is executed by a processor to implement a battery state detection method.

It will be appreciated by those skilled in the art that the structure shown in fig. 14 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements are applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In an embodiment, a computer device is further provided, including a memory and a processor, where the memory stores a computer program, and the processor implements the technical solution in the embodiment of the battery state detection method of the present application when executing the computer program, and the implementation principle and technical effects are similar, and are not repeated herein.

In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, where the computer program when executed by a processor implements the technical solution of the battery state detection method of the present application, and the implementation principle and technical effects are similar, and are not repeated herein.

In one embodiment, a computer program product is provided, which includes a computer program, where the computer program when executed by a processor implements the technical solution of the battery state detection method of the present application, and the implementation principle and technical effects are similar, and are not repeated herein.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

It should be noted that the above embodiments are only used to illustrate the technical solution of the present application, but not to limit the technical solution of the present application, and although the detailed description of the present application is given with reference to the above embodiments, it should be understood by those skilled in the art that the technical solution described in the above embodiments may be modified or some or all technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiments of the present application, and all the modifications or substitutions are included in the scope of the claims and the specification of the present application. In particular, the technical features mentioned in the respective embodiments may be combined in any manner as long as there is no structural conflict. The present application is not limited to the specific embodiments disclosed herein, but encompasses all technical solutions falling within the scope of the claims.

Claims

1. A battery status detection method, characterized in that the method comprises:

Obtain low-frequency noise test data of the battery;

Determining a test result of the battery under the target state indicator according to the low-frequency noise test data and a detection strategy of the target state indicator; the target state indicator includes a plurality of different battery state indicators;

Determining a test result of the battery under the target state indicator according to the low-frequency noise test data and a detection strategy for the target state indicator includes:

Obtaining a noise evaluation value of the target state indicator based on the low-frequency noise test data; the noise evaluation value represents a noise level of the battery in a healthy state under the target state indicator;

Determining a detection result of the battery under the target state indicator according to the noise evaluation value and the detection strategy of the target state indicator;

The step of obtaining the noise evaluation value of the target state indicator according to the low-frequency noise test data includes:

determining a noise evaluation value of the target state indicator according to noise power spectrum data of the battery;

The noise power spectrum data includes a plurality of power spectrum density frequency curves at a plurality of different voltages or a plurality of different currents; and determining the noise evaluation value of the target state indicator based on the noise power spectrum data of the battery includes at least one of the following methods:

Determining a noise evaluation value of the target state indicator according to a variation between power spectrum density values at a specified frequency in the plurality of power spectrum density frequency curves;

determining a noise evaluation value of the target state indicator according to a variation between amplitudes of power spectrum density values within a third preset frequency range in the plurality of power spectrum density frequency curves;

determining a noise evaluation value of the target state indicator according to a variation between the corner frequencies of the plurality of power spectrum density frequency curves;

The noise evaluation value of the target state indicator is determined according to the variation between the slopes within a fourth preset frequency range in the multiple power spectrum density frequency curves.

2. The method according to claim 1, wherein the noise power spectrum data comprises a power spectrum density frequency curve under a single voltage or a single current; and determining the noise evaluation value of the target state indicator based on the noise power spectrum data of the battery comprises at least one of the following methods:

According to the power spectrum density frequency curve, the power spectrum density value at the specified frequency is determined as the noise evaluation value of the target state indicator;

According to the power spectrum density frequency curve, the amplitude of the power spectrum density value within a first preset frequency range is determined as the noise evaluation value of the target state indicator;

Determining the turning frequency of the power spectrum density frequency curve as the noise evaluation value of the target state indicator;

The slope of the power spectrum density frequency curve within a second preset frequency range is determined as the noise evaluation value of the target state indicator.

3. The method according to claim 1, wherein obtaining the noise evaluation value of the target state indicator according to the low-frequency noise test data comprises:

Determine the low-frequency noise test data as the noise evaluation value of the target state indicator; or,

The noise evaluation value of the target state indicator is determined according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located.

4. The method according to claim 3, wherein determining the noise evaluation value of the target state indicator based on the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located comprises:

determining a noise correlation coefficient between the battery and adjacent batteries based on the low-frequency noise test data of the battery and the low-frequency noise test data of the other batteries; wherein the noise correlation coefficient indicates a degree of noise deviation between the battery and the adjacent batteries;

The noise correlation coefficient is determined as the noise evaluation value of the target state indicator.

5. The method according to any one of claims 1 to 4, wherein determining the detection result of the battery under the target state indicator based on the noise evaluation value and the detection strategy of the target state indicator comprises:

Obtaining a noise critical condition corresponding to the battery in a target critical state;

A detection result of the battery under the target state indicator is determined according to the noise evaluation value and the noise critical condition.

6. The method according to claim 5, wherein determining the detection result of the battery under the target state indicator according to the noise evaluation value and the noise critical condition comprises:

If the noise evaluation value does not meet the noise critical condition, determining that the detection result of the battery under the target state indicator is abnormal;

If the noise evaluation value satisfies the noise critical condition, it is determined that the detection result of the battery under the target state indicator is normal.

7. The method according to claim 6, wherein the noise critical condition is a noise threshold, and the method further comprises:

Obtaining a low-frequency noise test average value between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries in the module where the battery is located;

The low-frequency noise test average value is determined as the noise threshold.

8. The method according to claim 5, wherein the noise critical condition includes a first threshold, a second threshold, and a third threshold;

Determining, according to the noise evaluation value and the noise critical condition, a test result of the battery under the target state indicator, including:

If the noise evaluation value exceeds the first threshold and does not exceed the second threshold, determining that the detection result of the battery under the target state indicator is suspected to be abnormal;

If the noise evaluation value exceeds the second threshold and does not exceed the third threshold, determining that the detection result of the battery under the target state indicator is slightly abnormal;

If the noise evaluation value exceeds the third threshold, it is determined that the detection result of the battery under the target state indicator is seriously abnormal.

9. The method according to any one of claims 1 to 4, further comprising:

Obtaining the backend processing strategy corresponding to the target status indicator;

If the detection result of the battery under the target state indicator is abnormal, the battery is processed according to the back-end processing strategy corresponding to the target state indicator.

10. The method according to claim 9, wherein the abnormality includes suspected abnormality, minor abnormality, and serious abnormality; and processing the battery according to the back-end processing strategy corresponding to the target status indicator comprises:

If the test result of the battery under the target state indicator is suspected to be abnormal, confirming the test result of the battery under the target state indicator through secondary confirmation;

If the detection result of the battery under the target state indicator is slightly abnormal, repairing the battery through a repair operation;

If the detection result of the battery under the target state indicator is seriously abnormal, the battery is scrapped through a scrapping operation.

11. The method according to any one of claims 1 to 4, wherein obtaining low-frequency noise test data of the battery comprises:

Obtaining the voltage or current fluctuation data over time during the discharge of the battery at a specific current through a pre-built low-frequency noise test system to obtain the low-frequency noise test data; or

The low-frequency noise test data is obtained by detecting the fluctuation data of the voltage or current of the battery over time through an integrated test chip.

12. The method according to claim 11, characterized in that before obtaining the low-frequency noise test data of the battery, the method further comprises:

If the conditions are met, the step of obtaining the low-frequency noise test data of the battery is performed;

If not, the battery is adjusted to meet the voltage consistency requirement through charging and discharging equipment.

13. A battery status detection device implemented using the method according to any one of claims 1 to 4, characterized in that the device comprises:

A test data acquisition module is used to obtain low-frequency noise test data of the battery;

The detection result determination module is used to determine the detection result of the battery under the target state indicator according to the low-frequency noise test data and the detection strategy of the target state indicator; the target state indicator includes multiple different battery state indicators.

14. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 12 when executing the computer program.

15. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

16. A computer program product, comprising a computer program, characterized in that when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.