Battery Status Detection Method, Device, Computer Equipment, Storage Medium and Product

Abstract

This application relates to a battery state detection method, device, computer device, storage medium and product. The method includes: obtaining low-frequency noise test data of the battery, and determining a detection result of the battery under a target state index according to the low-frequency noise test data and a detection strategy of the target state index. By using this method, low-frequency noise test data of the battery can be obtained through the low-frequency noise test method. The low-frequency noise test method is sensitive to the data to be measured of the battery and has a high test efficiency, so that the detection efficiency of the battery state can be improved. At the same time, multiple different battery state indexes are detected. Different battery state indexes reflect the health state of the battery in different usage scenarios. In this way, multiple different battery state indexes can comprehensively reflect the health state of the battery, thereby improving the comprehensiveness of the battery state detection result.

Description

Technical Field

[0001] This application relates to the technical field of batteries, and particularly to a battery state detection method, device, computer device, storage medium, and product. Background Art

[0002] With the continuous development of new energy technologies, batteries are increasingly widely used in production and life. Therefore, the research on the health status and lifespan of batteries has received increasing attention.

[0003] Taking the battery health status as an example, in the related art, the battery health status is mainly detected by electrical testing methods such as charge and discharge.

[0004] However, when detecting the battery health status in the related art, the detection efficiency is too low and the detection results are not comprehensive enough. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a battery state detection method, device, computer device, storage medium, and product, which can improve the detection efficiency and comprehensiveness of the battery health status.

[0006] In a first aspect, an embodiment of the present application provides a battery state detection method, which includes:

[0007] Obtain low-frequency noise test data of the battery;

[0008] Determine the 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; the target state index includes multiple different battery state indexes.

[0009] In the battery state detection method in the embodiments of the present application, low-frequency noise test data of the battery is obtained, and according to the low-frequency noise test data and the detection strategy of the target state index, the detection result of the battery under the target state index is determined. This method obtains the low-frequency noise test data of the battery through the low-frequency noise test method. The low-frequency noise test method is sensitive to the data to be measured of the battery and has a high test efficiency, so that the detection efficiency of the battery state can be improved, and abnormal batteries can be detected in time, improving the safety of the battery; in addition, this method can detect multiple different battery state indexes at the same time. Different battery state indexes reflect the health state of the battery in different usage scenarios. In this way, multiple different battery state indexes can comprehensively reflect the health state of the battery, thus improving the comprehensiveness of the battery state detection result. Moreover, the same detection strategy or different detection strategies can be set for different battery state indexes. In this way, the flexible and rich detection strategies ensure the diversity of the battery state detection method. Furthermore, in this method, because low-frequency noise test data is used for analysis and detection, low-frequency noise test data can be collected for any type of battery. Therefore, the battery state detection method can be applied to the detection of multiple types of battery states, improving the wide applicability of the battery state detection method.

[0010] In one of the embodiments, according to the low-frequency noise test data and the detection strategy of the target state index, determining the detection result of the battery under the target state index includes:

[0011] According to the low-frequency noise test data, obtain the noise evaluation value of the target state index; the noise evaluation value characterizes the noise level of the health state of the battery under the target state index;

[0012] According to the noise evaluation value and the detection strategy of the target state index, determine the detection result of the battery under the target state index.

[0013] In the technical solution of the embodiments of the present application, 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, and the detection strategy corresponding to the target state index is used to determine the detection result of the battery under the target state index through the noise evaluation value. This method can improve the speed and efficiency of the battery state detection result on the basis of using the low-frequency noise test method, and can adopt the corresponding detection strategy for different state indexes to implement the battery state detection, making the indexes of the battery state detection not single, thus increasing the application range of the battery state detection scenario.

[0014] In one of the embodiments, according to the low-frequency noise test data, obtaining the noise evaluation value of the target state index includes:

[0015] Perform frequency domain conversion processing on the low-frequency noise test data to obtain the noise power spectrum data of the battery;

[0016] Determine the noise evaluation value of the target state index according to the noise power spectrum data of the battery.

[0017] In the technical solution of the embodiment of the present application, 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 can be determined according to the noise power spectrum data of the battery; this method can convert the time-domain data of the tested battery into frequency-domain data, that is, convert the low-frequency noise test data into noise power spectrum data, and obtain the corresponding frequency-domain evaluation value of the battery under the target state index through the frequency-domain data, that is, the noise evaluation value, and further complete the battery state detection process based on the frequency-domain evaluation value, so that the battery state detection process does not need to solve complex calculus equations, and only needs to pass through a simple processing process to achieve battery state detection, thereby reducing the amount of computation in the battery state detection process and improving the speed and efficiency of battery state detection.

[0018] In one embodiment, the noise power spectrum data includes the power spectral density frequency curve under a single voltage or a single current; determining the noise evaluation value of the target state index according to the noise power spectrum data of the battery includes at least one of the following methods:

[0019] According to the power spectral density frequency curve, determine the power spectral density value at the specified frequency as the noise evaluation value of the target state index;

[0020] According to the power spectral density frequency curve, determine the amplitude of the power spectral density value within the first preset frequency range as the noise evaluation value of the target state index;

[0021] Determine the turning frequency of the power spectral density frequency curve as the noise evaluation value of the target state index;

[0022] Determine the slope of the power spectral density frequency curve within the second preset frequency range as the noise evaluation value of the target state index.

[0023] In the technical solution of the embodiment of the present application, the noise evaluation value of the target state index can be determined in multiple ways according to the obtained power spectral density frequency curve of the battery under a single voltage or current, so that the best method can be selected according to the actual application requirements to determine the noise evaluation value of the target state index, so as to improve the speed and efficiency of determining the noise evaluation value of the target state index and simplify the process of determining the noise evaluation value.

[0024] In one embodiment, the noise power spectrum data includes multiple power spectral density frequency curves under multiple different voltages or multiple different currents; determining the noise evaluation value of the target state index according to the noise power spectrum data of the battery includes at least one of the following methods:

[0025] Determine the noise evaluation value of the target state index according to the change amount between the power spectral density values at a specified frequency among multiple power spectral density frequency curves;

[0026] Determine the noise evaluation value of the target state index according to the change amount between the amplitudes of the power spectral density values within a third preset frequency range among multiple power spectral density frequency curves;

[0027] Determine the noise evaluation value of the target state index according to the change amount between the break frequencies of multiple power spectral density frequency curves;

[0028] Determine the noise evaluation value of the target state index according to the change amount between the slopes within a fourth preset frequency range among multiple power spectral density frequency curves.

[0029] In the technical solution of the embodiment of the present application, the noise evaluation value of the target state index can be determined in multiple ways according to the obtained power spectral density frequency curves of the battery under multiple different voltages or multiple different currents, so that the best way can be selected according to the actual application requirements to determine the noise evaluation value of the target state index, thereby improving the speed and efficiency of determining the noise evaluation value of the target state index and simplifying the process of determining the noise evaluation value; moreover, this method can not only determine the noise evaluation value of the target state index through the power spectral density frequency curve of the battery under a single voltage or a single current, but also determine the noise evaluation value of the target state index through the power spectral density frequency curves of the battery under multiple different voltages or multiple different currents, thus increasing the diversity of the methods for determining the noise evaluation value of the target state index and making the method for determining the noise evaluation value of the target state index more flexible.

[0030] In one embodiment, obtaining the noise evaluation value of the target state index according to the low-frequency noise test data includes:

[0031] Determine the low-frequency noise test data as the noise evaluation value of the target state index; or,

[0032] Determine 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.

[0033] In the technical solution of the embodiment of the present application, the noise evaluation value of the target state index can be determined in different ways according to actual needs, making the process of determining the noise evaluation value of the target state index relatively flexible.

[0034] 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:

[0035] Based on the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries, determine the noise correlation coefficient between the battery and its adjacent batteries; the noise correlation coefficient represents the degree of noise deviation between the battery and its adjacent batteries;

[0036] Determine the noise evaluation value of the target state index as the noise correlation coefficient.

[0037] In the technical solution of the embodiment of the present application, the noise correlation coefficient between the battery and its adjacent batteries 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 as the noise evaluation value of the target state index, so that the method can not only determine the noise evaluation value of the target state index by taking the average value, but also determine the noise evaluation value of the target state index by calculating the noise correlation coefficient between the battery and its adjacent batteries, further increasing the determination methods of the noise evaluation value of the target state index and making the determination methods of the noise evaluation value of the target state index more flexible; at the same time, when determining the noise evaluation value of the target state index, the method does not need to consider the test data of other batteries in the module, thus improving the accuracy of the noise evaluation value.

[0038] In one embodiment, according to the noise evaluation value and the detection strategy of the target state index, determine the detection result of the battery under the target state index, including:

[0039] Obtain the noise critical condition corresponding to the battery under the target critical state;

[0040] According to the noise evaluation value and the noise critical condition, determine the detection result of the battery under the target state index.

[0041] In the technical solution of the embodiment of the present application, the noise critical condition corresponding to the battery under the target critical state can be obtained, and 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 in the ideal state, so that the determined detection result is relatively accurate and the accuracy of the detection result of the battery under the target state index is improved.

[0042] In one embodiment, according to the noise evaluation value and the noise critical condition, determine the detection result of the battery under the target state index, including:

[0043] If the noise evaluation value does not meet the noise critical condition, determine that the detection result of the battery under the target state index is abnormal;

[0044] If the noise evaluation value meets the noise critical condition, determine that the detection result of the battery under the target state index is normal.

[0045] In the technical solution of the embodiment of the present 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. Since the noise critical condition is the noise critical condition of the battery in the ideal state, determining the detection result by comparing the noise evaluation value with the noise critical condition can improve the accuracy of the detection result.

[0046] In one embodiment, the noise critical condition is a noise threshold, and the method further includes:

[0047] Obtain the average value of the low-frequency noise test data 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;

[0048] Determine the average value of the low-frequency noise test as the noise threshold.

[0049] In the technical solution of the embodiment of the present application, on the basis of obtaining 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 also be obtained, and the average value of the low-frequency noise test data between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries can be obtained, and then the average value of the low-frequency noise test is determined as the noise threshold; this process does not require a target critical state battery to determine the noise critical condition, making the process of determining the noise critical condition simple, convenient, and fast.

[0050] In one embodiment, the noise critical condition includes a first threshold, a second threshold, and a third threshold;

[0051] Determining the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition includes:

[0052] If the noise evaluation value exceeds the first threshold and does not exceed the second threshold, it is determined that the detection result of the battery under the target state index is suspected of being abnormal;

[0053] If the noise evaluation value exceeds the second threshold and does not exceed the third threshold, it is determined that the detection result of the battery under the target state index is slightly abnormal;

[0054] If the noise evaluation value exceeds the third threshold, it is determined that the detection result of the battery under the target state index is severely abnormal.

[0055] In the technical solution of the embodiment of the present application, different levels of threshold values can be set to determine different degrees of abnormal detection results of the battery under the target state index, so that the level of the determined abnormal detection result is more refined, and a precise basis is provided for selecting effective solutions when solving the abnormality.

[0056] In one embodiment, the above method further includes:

[0057] Obtain the backend processing strategy corresponding to the target status indicator;

[0058] If the detection result of the battery under the target status indicator is abnormal, the battery is processed according to the backend processing strategy corresponding to the target status indicator.

[0059] In the technical solution of the embodiment of the present application, when it is initially determined that the detection result is suspected to be abnormal, the detection result of the battery under the target status indicator can be confirmed again to ensure the accuracy of the detection result, so as to avoid the problem of misprocessing normal batteries by adopting the backend processing strategy, save resources, and also avoid the problem that the abnormal battery is not processed in time without adopting the backend processing strategy, resulting in low battery safety; at the same time, this method can adopt corresponding backend processing strategies according to different levels of abnormal detection results to process the battery accordingly, which can not only prevent inferior batteries from flowing into the market and causing safety accidents, but also prevent abnormal batteries from being used normally during the use stage and causing customer complaint incidents, further improving the safety of the battery and increasing the user experience.

[0060] In one embodiment, the abnormality includes suspected abnormality, slight abnormality, and serious abnormality; processing the battery according to the backend processing strategy corresponding to the target status indicator includes:

[0061] If the detection result of the battery under the target status indicator is suspected to be abnormal, the detection result of the battery under the target status indicator is determined by means of secondary confirmation;

[0062] If the detection result of the battery under the target status indicator is slightly abnormal, the battery is repaired through a repair operation;

[0063] If the detection result of the battery under the target status indicator is seriously abnormal, the battery is scrapped through a scrapping operation.

[0064] In the technical solution of the embodiment of the present application, different backend processing strategies can be adopted to process different degrees of abnormal batteries correspondingly to prevent safety accidents in a timely manner, and the user experience can also be increased.

[0065] In one embodiment, obtaining the low-frequency noise test data of the battery includes:

[0066] Obtain the fluctuation data of the voltage or current of the battery over time during the discharge process at a specific current through a pre-built low-frequency noise test system to obtain the low-frequency noise test data; or,

[0067] Detect the fluctuation data of the voltage or current of the battery over time through an integrated test chip to obtain the low-frequency noise test data.

[0068] In the technical solution of the embodiment of the present application, the low-frequency noise test data of the battery can be obtained through various methods, which improves the flexibility of obtaining the low-frequency noise test data of the battery.

[0069] In one embodiment, before obtaining the low-frequency noise test data of the battery, the above method further includes:

[0070] Detecting whether the voltage of the battery meets the voltage consistency requirement;

[0071] If it meets the requirement, execute the step of obtaining the low-frequency noise test data of the battery;

[0072] If it does not meet the requirement, adjust the battery to meet the voltage consistency requirement through a charge and discharge device.

[0073] In the technical solution of the embodiment of the present application, it is possible to detect whether the voltage of the battery meets the voltage consistency requirement. When it does not meet the requirement, the voltages of each battery in the module are set to be the same, so that the battery states of all batteries in the same module detected are relatively accurate.

[0074] In a second aspect, the embodiment of the present application provides a battery state detection device, which includes:

[0075] A test data acquisition module, configured to acquire low-frequency noise test data of the battery;

[0076] A detection result determination module, configured to determine the detection result of the battery under the target state index according to the detection strategy of the low-frequency noise test data and the target state index; the target state index includes multiple different battery state indexes.

[0077] In a third aspect, the present application further provides a computer device. The charging control device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method according to any one of the first aspects are implemented.

[0078] In a fourth aspect, the present application further provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method according to any one of the first aspects are implemented.

[0079] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of the first aspects are implemented.

[0080] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific implementation manners of the present application are specifically exemplified below. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 FIG. 1 is an application environment diagram of a battery state detection method in an embodiment;

[0082] Figure 2 FIG. 2 is a schematic flowchart of a battery state detection method in an embodiment;

[0083] Figure 3 FIG. 3 is a schematic flowchart of a battery state detection method in another embodiment;

[0084] Figure 4 FIG. 4 is a schematic flowchart of a battery state detection method in another embodiment;

[0085] Figure 5 FIG. 5 is a power spectral density frequency curve graph under a single voltage or a single current in another embodiment;

[0086] Figure 6 FIG. 6 is another power spectral density frequency curve graph under a manufacturing defect degree in another embodiment;

[0087] Figure 7 FIG. 7 is another power spectral density frequency curve graph under a self-discharge degree in another embodiment;

[0088] Figure 8 FIG. 8 is a schematic flowchart of a battery state detection method in another embodiment;

[0089] Figure 9 FIG. 9 is a schematic flowchart of a battery state detection method in another embodiment;

[0090] Figure 10 FIG. 10 is a schematic flowchart of a battery state detection method in another embodiment;

[0091] Figure 11 FIG. 11 is a schematic flowchart of a battery state detection method in another embodiment;

[0092] Figure 12 FIG. 12 is a schematic flowchart of a battery state detection method in another embodiment;

[0093] Figure 13 FIG. 13 is a structural block diagram of a battery state detection device in an embodiment;

[0094] Figure 14 FIG. 14 is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0095] The embodiments of the technical solution of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solution of the present application more clearly, so they are only examples and cannot be used to limit the protection scope of the present application.

[0096] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the term "including" and any variation thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion.

[0097] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0098] With the continuous development of new energy technologies, batteries are increasingly widely used in production and life. Therefore, the health status and lifespan of batteries are attracting more and more attention. Taking the health status of batteries as an example, in related technologies, the charge-discharge parameters of batteries can be detected by electrical testing methods such as charge and discharge, and then the health status of the batteries can be evaluated based on the charge-discharge parameters of the batteries. Further, defective batteries, such as batteries with poor K values, etc., can be screened out during the battery manufacturing stage, and abnormal batteries, such as batteries with high self-discharge levels, lithium metal deposition batteries, internal short-circuit batteries, etc., can be screened out during the battery usage stage to improve the safety during the battery usage stage.

[0099] Generally, electrical testing methods such as charge and discharge are used to detect the health status of batteries. However, the speed of detecting the charge-discharge parameters of batteries by electrical testing methods such as charge and discharge is slow, and the detection of charge-discharge parameters is insensitive, so that the charge-discharge parameters of batteries cannot be detected in time, and thus the health status of the batteries cannot be evaluated in time, resulting in a low detection efficiency of battery status. At the same time, the detection methods of electrical testing methods such as charge and discharge are single, only applicable to the detection of specific types of battery status and only applicable to the detection of batteries in specific states, resulting in incomplete detection results of battery status.

[0100] Based on this, in order to solve the problems of low efficiency in detecting the battery state and incomplete detection results of the battery state, the applicant has conducted research and proposed a battery state detection method. By using the low-frequency noise test method to detect the battery state, it can not only improve the detection efficiency of the battery state, but also adopt different detection strategies for different types of batteries and different detection indicators of the battery to achieve battery state detection, thereby improving the comprehensiveness of the battery state detection results.

[0101] As Figure 1 shown, it is the application environment of the battery state detection method provided by the embodiment of the present application. This application environment includes a computer device 11, a low-frequency noise test system 12, and a battery 13 to be detected. Among them, the low-frequency noise test system 12 includes a variable resistor 121, a low-frequency noise test device 122, and an amplifier 123. Here, the low-frequency noise test device 122 can be a low-frequency noise tester or an oscilloscope, etc. Among them, the battery 13, the variable resistor 121, the low-frequency noise test device 122, and the amplifier 123 are electrically connected, and the specific connection method is as Figure 1 shown, and in the embodiment of the present application, the low-frequency noise test device 122 and the computer device 11 are communicatively connected, and are used for the low-frequency noise test device 122 to send the detected test data to the computer device 11. This connection method can be Bluetooth, mobile network, wifi, etc. Optionally, the battery 13 to be detected can be one or more batteries in a module. Figure 1 In Figure 1 it, a single battery is used as an example for illustration, and the detection process is not limited to a single battery. At the same time, during the detection of the battery, the battery is placed depending on the metal box. Figure 1 The metal box is also illustrated in

[0102] It should be noted here that the battery state detection method provided by the embodiment of the present application can be implemented not only through the Figure 1 shown application environment, but also through an application environment including a computer device, an integrated test chip, and a battery to be detected. Among them, the integrated test chip is a test chip integrating the variable resistor, the low-frequency noise test device, and the amplifier in the low-frequency noise test system 12, and the computer device and the integrated test chip are communicatively connected, and are used for the integrated test chip to send the detected test data to the computer device.

[0103] In one embodiment, Figure 2 It is the flow chart of the battery state detection method in an embodiment of the present application. In the embodiment of the present application, the execution subject of this method is used as Figure 1 the computer device in Figure 2 for illustration. As

[0104] S100. Obtain the low-frequency noise test data of the battery.

[0105] In the embodiment of the present application, the computer device obtains the current low-frequency noise test data of the battery. Among them, the battery can be a battery made of different cathode materials, such as ternary batteries, lithium iron phosphate batteries, etc., and can also be an ion battery (such as lithium-ion battery, sodium-ion battery, potassium-ion battery, zinc-ion battery, etc.), a metal-based battery (such as lithium metal battery, sodium metal battery, potassium metal battery, etc.) and a non-negative electrode battery, etc. No limitation is imposed on the battery type embodiment.

[0106] In one embodiment, the method for obtaining the low-frequency noise test data of the battery can be to directly detect the current low-frequency noise test data of the battery through a low-frequency noise detection device, and then the computer device obtains the current low-frequency noise test data of the battery detected by the low-frequency noise detection device in real time.

[0107] In another embodiment, the method for obtaining the low-frequency noise test data of the battery can also be that the computer device obtains the low-frequency noise test data of the battery pre-stored within a historical time period from local or cloud, and then predicts the current low-frequency noise test data of the battery through the low-frequency noise test data within the historical time period.

[0108] S200. Determine the 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. Among them, the target state index includes multiple different battery state indexes.

[0109] In the embodiment of the present application, the target state index includes two different battery state indexes, namely the first state index and the second state index. Among them, one of the first state index and the second state index represents an index that affects the battery health state during the manufacturing stage, and the other state index represents an index that affects the battery health state during the use stage.

[0110] In the embodiment of the present application, the target state index is described by using the first state index to represent an index that affects the battery health state during the manufacturing stage and the second state index to represent an index that affects the battery health state during the use stage. Therefore, the above-mentioned first state index can be the maximum charge state, the maximum energy state, etc. of the battery during the manufacturing stage, and the above-mentioned second state index can also be the maximum charge state, the maximum energy state, etc. of the battery during the use stage.

[0111] Among them, the first state index can include the functional state, etc., and the second state index can include the state of charge, the power state, the functional state, the remaining power, etc. However, in the embodiment of the present application, the first state index is the manufacturing defect degree, and the above-mentioned second state index is the self-discharge degree, the aging degree, the lithium plating degree and the internal short circuit degree.

[0112] Optionally, the detection strategy for the above target state indicators can be a pre-constructed detection strategy. Among them, for the same target state indicator, different detection strategies can be used to detect the battery states of different types of batteries. That is, the detection strategies corresponding to the same target state indicator for different types of batteries can be different; for the same target state indicator, the same detection strategy can be used to detect the battery states of different types of batteries. That is, the detection strategies corresponding to different types of batteries can be the same.

[0113] At the same time, for the same type of battery, different detection strategies can be used to detect the battery states under different target state indicators. That is, the detection strategies corresponding to different target state indicators for the same type of battery can be different; for the same type of battery, the same detection strategy can be used to detect the battery states under different target state indicators. That is, the detection strategies corresponding to different target state indicators for the same type of battery can be the same.

[0114] In addition, for different types of batteries, the same detection strategy can be used to detect the battery states under different target state indicators. That is, the detection strategies corresponding to different target state indicators for different types of batteries can be the same.

[0115] Among them, the detection strategy for the target state indicator can be an algorithm model detection method or a comparative analysis detection method, etc. Optionally, the above method of determining the detection result of the battery under the target state indicator according to the low-frequency noise test data and the algorithm model detection method can be to pre-train an algorithm model corresponding to the target state indicator, input the low-frequency noise test data obtained in the above steps into the algorithm model, and output the detection result of the battery under the target state indicator through the algorithm model.

[0116] Optionally, the above method of determining the detection result of the battery under the target state indicator according to the low-frequency noise test data and the comparative analysis detection method can be to obtain a pre-constructed corresponding information library, search for low-frequency noise data matching the current low-frequency noise test data of the battery in the corresponding information library, and determine the detection result corresponding to the matched low-frequency noise data as the detection result of the battery under the target state indicator. Optionally, the corresponding information library can include low-frequency noise data of different types of batteries, different state indicators, detection results, and the corresponding relationships among the three.

[0117] It should be noted here that the detection result of the battery under the target state indicator can be normal or abnormal, and the specific detection result is determined according to the actual state of the battery.

[0118] In the battery state detection method according to the embodiments of the present application, low-frequency noise test data of the battery is obtained, and according to the low-frequency noise test data and the detection strategy of the target state index, the detection result of the battery under the target state index is determined. This method obtains the low-frequency noise test data of the battery through the low-frequency noise test method. The low-frequency noise test method is sensitive to the data to be measured of the battery and has a high test efficiency, so that the detection efficiency of the battery state can be improved, and abnormal batteries can be detected in time, improving the safety of the battery; in addition, this method can detect multiple different battery state indexes at the same time. Different battery state indexes reflect the health state of the battery in different usage scenarios. In this way, multiple different battery state indexes can comprehensively reflect the health state of the battery, thus improving the comprehensiveness of the battery state detection result. Moreover, the same detection strategy or different detection strategies can be set for different battery state indexes. In this way, the flexible and rich detection strategies ensure the diversity of the battery state detection methods. Furthermore, in this method, because low-frequency noise test data is used for analysis and detection, low-frequency noise test data can be collected for any type of battery. Therefore, the battery state detection method can be applied to the detection of multiple types of battery states, improving the wide applicability of the battery state detection method.

[0119] Based on the above embodiments, the process of determining the 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 will be described below. In one embodiment, as Figure 3 shown, the above S200 includes the following steps:

[0120] S210. Obtain the noise evaluation value of the target state index according to the low-frequency noise test data; the noise evaluation value characterizes the noise level of the health state of the battery under the target state index.

[0121] In the embodiments of the present application, it is necessary to first obtain the noise evaluation value of the target state index according to the low-frequency noise test data and the target state index of the battery, and further process the noise evaluation value of the target state index to obtain the detection result of the battery under the target state index.

[0122] Among them, the noise evaluation value refers to the noise level of the health state of the battery under the target state index, which 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, then the noise evaluation value can be a parameter that can accurately evaluate the battery aging degree. Another example is that if the target state index is the self-discharge degree of the battery, then the noise evaluation value must be a parameter that can accurately evaluate the self-discharge degree of the battery.

[0123] Exemplarily, the method for obtaining the noise evaluation value of the target state index based on the low-frequency noise test data can 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 method for obtaining the noise evaluation value of the target state index based on the low-frequency noise test data can also be to calculate the noise-related indexes through the low-frequency noise test data of the battery, and determine the noise evaluation value of the target state index according to the calculated noise-related indexes. The above noise-related indexes can be noise intensity, signal-to-noise ratio, noise signal power, and so on.

[0124] S220. Determine 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.

[0125] Specifically, the detection strategy of the target state index pre-constructed can be obtained from local or cloud, and the detection strategy of the target state index is adopted to perform corresponding processing on the noise evaluation value of the battery under the target state index obtained in the above steps, so as to obtain the detection result of the battery under the target state index.

[0126] Or, a neural network detection model can also be trained based on the obtained detection strategy of the target state index, and then the noise evaluation value of the battery is input into the trained neural network detection model to output the detection result of the battery under the target state index.

[0127] The battery state detection method in the embodiments of the present 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 adopt the detection strategy corresponding to the target state index to determine the detection result of the battery under the target state index through the noise evaluation value. This method can improve the speed and efficiency of the battery state detection result on the basis of adopting the low-frequency noise test method, and can adopt corresponding detection strategies for different state indexes to implement the battery state detection, so that the indexes of the battery state detection are not single, thereby increasing the application range of the battery state detection scenarios.

[0128] In order to obtain the noise evaluation value of the target state index simply and quickly, in one embodiment, as Figure 4 shown, the step of obtaining the noise evaluation value of the target state index according to the low-frequency noise test data in the above S210 can be implemented through the following steps:

[0129] S211. Perform frequency-domain conversion processing on the low-frequency noise test data to obtain the noise power spectrum data of the battery.

[0130] In practical applications, in some scenarios, the amount of computation involved in the time-domain processing method is relatively large, and the relevant data of the battery cannot be accurately described with finite parameters. Therefore, the speed of battery state detection is slow, the efficiency is low, and the accuracy of the detection result is not high enough. The frequency-domain processing method can decompose complex data into the superposition of simple data and can more accurately describe the characteristics of the battery. Based on this, in order to improve the speed, efficiency, and accuracy of battery state detection, the frequency-domain processing method is used in the embodiments of this application to implement the battery state detection process.

[0131] Among them, the obtained low-frequency noise test data is time-domain data. Therefore, the computer device can first perform frequency-domain conversion processing on the low-frequency noise test data using the time-frequency conversion method to obtain the noise power spectrum data of the battery, and the noise power spectrum data here is frequency-domain data. Optionally, the time-frequency conversion method can be the Laplace transform method or the Z-transform method. However, in the embodiments of this application, the time-frequency conversion method is described as the Fourier transform method.

[0132] S212. Determine the noise evaluation value of the target state index according to the noise power spectrum data of the battery.

[0133] It should be noted here that the computer device can perform index operation processing on the noise power spectrum data of the battery to obtain the noise evaluation value of the target state index. At the same time, the computer device can 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 device can perform analysis processing and comparison processing on the noise power spectrum data of the battery to obtain the noise evaluation value of the target state index.

[0134] The battery state detection method in the embodiments of this 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; this method can convert the time-domain data of the tested battery into frequency-domain data, that is, convert the low-frequency noise test data into noise power spectrum data, and obtain the corresponding frequency-domain evaluation value of the battery under the target state index through the frequency-domain data, that is, the noise evaluation value, and further complete the battery state detection process based on the frequency-domain evaluation value, so that the battery state detection process does not need to solve complex calculus equations, and only needs to pass through a simple processing process to achieve battery state detection, thereby reducing the amount of computation in the battery state detection process and improving the speed and efficiency of battery state detection.

[0135] In some scenarios, a single charge and discharge voltage or a single charge and discharge current can be set for the battery to implement the battery status detection process. The following embodiments of the present application will describe how to determine the noise evaluation value of the target state indicator based on the battery's noise power spectrum data when a single charge and discharge voltage or a single charge and discharge current is set for the battery. In one embodiment, the noise power spectrum data includes a power spectrum density frequency curve under a single voltage or a single current; then the step of determining the noise evaluation value of the target state indicator based on the battery's noise power spectrum data in S212 can include at least one of the following methods:

[0136] 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 the first preset frequency range is determined as the noise evaluation value of the target state indicator; the turning frequency of the power spectrum density frequency curve is determined as the noise evaluation value of the target state indicator; and the slope of the power spectrum density frequency curve within the second preset frequency range is determined as the noise evaluation value of the target state indicator.

[0137] In an embodiment of the present application, at least one parameter in four dimensions can be selected from the power spectrum density value at a specified frequency in the power spectrum density frequency curve, the change between the amplitudes of the power spectrum density values within a first preset frequency range, the turning frequency, and the slope within a second preset frequency range to determine the noise evaluation value of the target state indicator.

[0138] The following describes the process of determining the power spectrum density value at a specified frequency as the noise evaluation value of the target state indicator based on the power spectrum density frequency curve.

[0139] During the low-frequency noise test, a single charge and discharge voltage or a single charge and discharge current can be set for the battery to obtain low-frequency noise test data under a single voltage or a single current. The power spectrum density frequency curve under a single voltage or a single current can be further obtained through the low-frequency noise test data under a single voltage or a single current.

[0140] The power spectrum density frequency curve under a single voltage or current is a curve formed by the power spectrum density corresponding to each frequency within a certain frequency range. A specified frequency can be selected from the frequency range, and the power spectrum density value corresponding to the specified frequency on the power spectrum density frequency curve is determined as the noise assessment value of the target state indicator. Optionally, the specified frequency can be any frequency within the frequency range corresponding to the power spectrum density frequency curve.

[0141] For example, Figure 5It is a power spectral density frequency curve graph under a single voltage or a single current. Each point on the power spectral density frequency curve corresponds to a set of values, including frequency and power spectral density value respectively, and the frequency and power spectral density value in each set of values correspond one by one. Figure 5 The frequency range corresponding to the power spectral density frequency curve in Figure 5 is [f1, f2], and the specified frequency can be any frequency between f1 and f2. If the specified frequency is f1, the corresponding power spectral density value is PSD1; if the specified frequency is f2, the corresponding power spectral density value is PSD2; if the specified frequency is f3, the corresponding power spectral density value is PSD3; the power spectral density values obtained for other specified frequencies are similar and will not be elaborated here.

[0142] The following will explain the process of determining the amplitude of the power spectral density value within the first preset frequency range as the noise evaluation value of the target state index based on the above power spectral density frequency curve.

[0143] Based on the power spectral density frequency curve under a single voltage or a single current obtained in the above steps, the power spectral density values within the first preset frequency range on the power spectral density frequency curve can be obtained first, and then the average value or median value, etc. of the power spectral density values within the first preset frequency range can be calculated to obtain the amplitude of the power spectral density values within the first preset frequency range, and this amplitude is determined as the noise evaluation value of the target state index.

[0144] In addition, based on the power spectral density frequency curve under a single voltage or a single current obtained in the above steps, the maximum power spectral density value and the minimum power spectral density value within the first preset frequency range on the power spectral density frequency curve can be obtained first, and then the difference between the maximum power spectral density value and the minimum power spectral density value within the first preset frequency range is calculated to obtain the amplitude of the power spectral density values within the first preset frequency range, and this amplitude is determined as the noise evaluation value of the target state index.

[0145] Furthermore, based on the power spectral density frequency curve under a single voltage or a single current obtained in the above steps, the power spectral density values corresponding to each frequency within the first preset frequency range on the power spectral density frequency curve can be obtained first, and then the difference between the power spectral density values of two frequencies in the first preset frequency range is calculated pairwise, and then the average value of all or part of the obtained frequency differences is calculated to obtain the amplitude of the power spectral density values within the first preset frequency range, and this amplitude is determined as the noise evaluation value of the target state index.

[0146] Continuing to refer to the previous example, if the first preset frequency range of the power spectral density frequency curve is [f1, f2], the power spectral density values within the first preset frequency range can be the power spectral density values corresponding to each point on the power spectral density frequency curve between f1 and f2. Among them, within the first preset frequency range, the frequency changes from f1 to f2 in ascending order. The power spectral density values corresponding to each frequency can be equal or unequal, and this embodiment does not make any limitations in this regard, and is not limited to Figure 5 the power spectral density values corresponding to each frequency shown in

[0147] Exemplarily, in the same test environment, after a certain defective battery under a single voltage or a single current is tested by low-frequency noise, the power spectral density frequency curve within the frequency range [f1, f2] obtained is as Figure 6 shown. Correspondingly, Figure 6 it also shows the power spectral density frequency curve of a normal battery within the frequency range [f1, f2]. Thus, it can be obtained that within the frequency range [f1, f2], the power spectral density values of the defective battery are all greater than those of the normal battery.

[0148] In the same test environment, the power spectral density frequency curves of a battery with a high self-discharge degree and a battery with a low self-discharge degree under a single voltage or a single current within a certain frequency range are as Figure 7 shown. Correspondingly, Figure 7 it also shows the power spectral density frequency curve of a battery with a medium self-discharge degree (normal battery). Thus, it can be obtained that within a certain frequency range, the power spectral density values of the battery with a high self-discharge degree are all greater than those of the normal battery, and within a certain frequency range, the power spectral density values of the normal battery are all greater than those of the battery with a low self-discharge degree.

[0149] In practical applications, a battery with a self-discharge degree lower than that of a battery with a medium self-discharge degree belongs to a qualified battery, and a battery with a self-discharge degree higher than that of a battery with a medium self-discharge degree belongs to an unqualified battery.

[0150] The following describes the process of determining the turning frequency of the power spectral density frequency curve as the noise evaluation value of the target state index.

[0151] Among them, the turning frequency of the above-mentioned power spectral density frequency curve refers to the frequency value of the turning point corresponding to the transition from a slanted line to an approximately horizontal line in the power spectral density frequency curve.

[0152] In one embodiment, the way to determine the turning frequency of the power spectral density frequency curve as the noise evaluation value of the target state index may be to first sequentially obtain all the power spectral density values in the order of the frequency magnitudes on the power spectral density frequency curve, then compare the power spectral density values corresponding to every two adjacent frequencies, determine the corresponding turning point on the power spectral density frequency curve according to the comparison result, and then determine the frequency corresponding to the turning point as the turning frequency of the power spectral density frequency curve, and determine the turning frequency as the noise evaluation value of the target state index.

[0153] In another embodiment, the way to determine the turning frequency of the power spectral density frequency curve as the noise evaluation value of the target state index may also be to calculate the slope of each point on the power spectral density frequency curve, then calculate the difference between the slopes of every two adjacent points to obtain the slope difference, and then select one of the two points with the largest slope difference, and determine the frequency corresponding to the selected point as the turning frequency of the power spectral density frequency curve, that is, obtain the noise evaluation value of the target state index.

[0154] The following describes the process of determining the slope of the power spectral density frequency curve within the second preset frequency range as the noise evaluation value of the target state index.

[0155] Among them, based on the power spectral density frequency curve of a single voltage or a single current obtained in the above steps, the slope of each point on the power spectral density frequency curve within the second preset frequency range can be calculated first, and then the average value of the slopes of all points within the second preset frequency range is obtained, and the slope of the power spectral density frequency curve within the second preset frequency range is obtained, and the slope of the power spectral density frequency curve within the second preset frequency range is determined as the noise evaluation value of the target state index.

[0156] Alternatively, based on the power spectral density frequency curve of a single voltage or a single current obtained in the above steps, any point on the power spectral density frequency curve within the second preset frequency range can be selected, the slope of the selected point can be calculated, and the slope of the point is determined as the noise evaluation value of the target state index.

[0157] In the embodiments of the present application, based on the power spectral density frequency curve of a single voltage or a single current obtained in the above steps, it is obtained by mathematically fitting all the power spectral density values within the second preset frequency range. [[ID=!]]

[0158] The battery state detection method in the embodiments of the present application can determine the noise evaluation value of the target state index in multiple ways according to the obtained power spectral density frequency curve of the battery under a single voltage or current, so that the best way can be selected according to the actual application requirements to determine the noise evaluation value of the target state index, so as to improve the speed and efficiency of determining the noise evaluation value of the target state index, and simplify the process of determining the noise evaluation value.

[0159] In some scenarios, multiple different charge and discharge voltages or multiple different charge and discharge currents of the battery can be set to implement the battery state detection process. Hereinafter, embodiments of the present application will introduce the process of determining the noise evaluation value of the target state index based on the noise power spectrum data of the battery when setting multiple different charge and discharge voltages or multiple different charge and discharge currents of the battery. In one embodiment, the above noise power spectrum data includes multiple power spectral density frequency curves at multiple different voltages or multiple different currents; then the step of determining the noise evaluation value of the target state index based on the noise power spectrum data in S212 above may include at least one of the following methods:

[0160] Determine the noise evaluation value of the target state index according to the change amount between the power spectral density values at a specified frequency among multiple power spectral density frequency curves; determine the noise evaluation value of the target state index according to the change amount between the amplitudes of the power spectral density values within a third preset frequency range among multiple power spectral density frequency curves; determine the noise evaluation value of the target state index according to the change amount between the break frequencies of multiple power spectral density frequency curves; determine the noise evaluation value of the target state index according to the change amount between the slopes within a fourth preset frequency range among multiple power spectral density frequency curves.

[0161] In the embodiments of the present application, at least one dimension parameter can be selected from the four dimension parameters of the change amount between the power spectral density values at a specified frequency, the change amount between the amplitudes of the power spectral density values within a third preset frequency range, the change amount between the break frequencies, and the change amount between the slopes within a fourth preset frequency range among multiple power spectral density frequency curves to determine the noise evaluation value of the target state index.

[0162] Hereinafter, the process of determining the noise evaluation value of the target state index according to the change amount between the power spectral density values at a specified frequency among multiple power spectral density frequency curves will be described first.

[0163] In practical applications, each single voltage or each single current corresponds to a power spectral density frequency curve. Therefore, multiple different voltages or multiple different currents respectively correspond to multiple power spectral density frequency curves.

[0164] For the power spectral density frequency curve corresponding to each single voltage or each single current, the power spectral density value at a specified frequency can be obtained according to the power spectral density frequency curve. The method of obtaining the power spectral density value at a specified frequency according to the power spectral density frequency curve here is similar to the method of obtaining the power spectral density value at a specified frequency according to the power spectral density frequency curve above, and the embodiments of the present application will not elaborate on this. Optionally, the specified frequency in this embodiment may be equal to or different from the specified frequency in the previous embodiment, and the embodiments of the present application do not make any limitation on this.

[0165] Further, based on the power spectral density values at the specified frequencies corresponding to each single voltage or each single current obtained, pairwise comparison processing can be performed on the power spectral density values among all the obtained power spectral density values to obtain the maximum power spectral density value and the minimum power spectral density value among all the power spectral density values, and the difference between the maximum power spectral density value and the minimum power spectral density value is determined as the variation amount between the power spectral density values corresponding to multiple different voltages or multiple different currents at the specified frequency, that is, the noise evaluation value of the target state index is obtained.

[0166] The following describes the process of determining the noise evaluation value of the target state index according to the variation amount between the amplitudes of the power spectral density values within the third preset frequency range in the above-mentioned multiple power spectral density frequency curves.

[0167] Based on the multiple power spectral density frequency curves corresponding to multiple different voltages or multiple different currents obtained in the above steps, for each power spectral density frequency curve, the amplitude of the power spectral density value of the power spectral density frequency curve within the third preset frequency range can be obtained according to the power spectral density frequency curve. The method of 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 here is similar to the method of 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 above, and the embodiments of the present application will not elaborate on this. Optionally, the third preset frequency range in this embodiment may be equal to or different from the first preset frequency range in the previous embodiment, and the embodiments of the present application do not make any limitation on this.

[0168] Further, based on the amplitudes of the power spectral density values within the third preset frequency range corresponding to each single voltage or each single current obtained, pairwise comparison processing can be performed on the amplitudes among all the obtained amplitudes of the power spectral density values to obtain the maximum amplitude and the minimum amplitude among all the amplitudes of the power spectral density values, and the difference between the maximum amplitude and the minimum amplitude is determined as the variation amount between the amplitudes of the power spectral density values corresponding to multiple different voltages or multiple different currents within the third preset frequency range, that is, the noise evaluation value of the target state index is obtained.

[0169] The process of determining the noise evaluation value of the target state index based on the variation amount between the break frequencies of multiple power spectral density frequency curves will be described below.

[0170] Based on the multiple power spectral density frequency curves corresponding to multiple different voltages or multiple different currents obtained in the above steps, for each power spectral density frequency curve, the break frequency of the power spectral density frequency curve can be obtained according to the power spectral density frequency curve. The method of obtaining the break frequency of the power spectral density frequency curve according to the power spectral density frequency curve here is similar to the method of obtaining the break frequency of the power spectral density frequency curve according to the power spectral density frequency curve above, and this will not be elaborated in the embodiments of the present application.

[0171] Further, based on the break frequencies of the power spectral density frequency curves corresponding to each single voltage or each single current obtained, the break frequencies of all the obtained power spectral density frequency curves can be compared and processed to obtain the maximum break frequency and the minimum break frequency among the break frequencies of all the power spectral density frequency curves, and the difference between the maximum break frequency and the minimum break frequency is determined as the variation amount between the break frequencies of the multiple power spectral density frequency curves corresponding to multiple different voltages or multiple different currents, that is, the noise evaluation value of the target state index is obtained. [[ID=***]]

[0172] The process of determining the noise evaluation value of the target state index based on the variation amount between the slopes within the fourth preset frequency range of multiple power spectral density frequency curves will be described below.

[0173] Based on the multiple power spectral density frequency curves corresponding to multiple different voltages or multiple different currents obtained in the above steps, for each power spectral density frequency curve, the slope of the power spectral density frequency curve within the fourth preset frequency range can be obtained according to the power spectral density frequency curve. The method of obtaining the slope of the power spectral density frequency curve within the fourth preset frequency range according to the power spectral density frequency curve here is similar to the method of obtaining the slope of the power spectral density frequency curve within the second preset frequency range in the previous embodiment, and this will not be elaborated in the embodiments of the present application. Optionally, the fourth preset frequency range in this embodiment may be equal to or different from the second preset frequency range in the previous embodiment, and this is not limited in the embodiments of the present application.

[0174] Further, based on the slopes within the fourth preset frequency range corresponding to each single voltage or each single current obtained, the slopes within all the fourth preset frequency ranges obtained can be compared to obtain the maximum slope and the minimum slope among all the slopes within the fourth preset frequency ranges, and the difference between the maximum slope and the minimum slope is determined as the variation amount between the slopes corresponding to multiple different voltages or multiple different currents within the fourth preset frequency range, that is, the noise evaluation value of the target state index is obtained.

[0175] In the battery state detection method of the embodiments of the present application, according to the power spectral density frequency curves of the battery under multiple different voltages or multiple different currents obtained, the noise evaluation value of the target state index can be determined in various ways. Thus, the best way can be selected according to the actual application requirements to determine the noise evaluation value of the target state index, so as to improve the speed and efficiency of determining the noise evaluation value of the target state index and simplify the process of determining the noise evaluation value. Moreover, this method can not only determine the noise evaluation value of the target state index through the power spectral density frequency curve of the battery under a single voltage or a single current, but also determine the noise evaluation value of the target state index through the power spectral density frequency curves of the battery under multiple different voltages or multiple different currents, thereby increasing the diversity of the methods for determining the noise evaluation value of the target state index and making the method for determining the noise evaluation value of the target state index more flexible.

[0176] The process of obtaining the noise evaluation value of the target state index based on the low-frequency noise test data in the above steps will be described below. In one embodiment, the steps in S210 above 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.

[0177] In the embodiments of the present application, in order to reduce the amount of calculation in the process of determining the noise evaluation value, the low-frequency noise test data can be directly determined as the noise evaluation value of the target state index.

[0178] However, directly determining the low-frequency noise test data as the noise evaluation value of the target state index may lead to the problem that the battery state detection result is not very accurate. Based on this, in the embodiments of the present application, the noise evaluation value of the target state index can also be 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.

[0179] Among them, the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries can be compared pairwise to obtain the maximum or 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 or minimum low-frequency noise test data is determined as the noise evaluation value of the target state index.

[0180] Alternatively, the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries can be weighted and summed, and the weighted sum result is determined as the noise evaluation value of the target state index.

[0181] In the battery state detection method according to the embodiments of the present application, the noise evaluation value of the target state index can be determined in different ways according to actual needs, making the process of determining the noise evaluation value of the target state index relatively flexible.

[0182] In practical applications, if it is necessary to greatly reduce the computational amount in the battery state detection process, the adjacent noise correlation coefficient method can also be used to determine the noise evaluation value of the target state index. The process of determining the noise evaluation value of the target state index by using the adjacent noise correlation coefficient method will be described below. In one embodiment, as Figure 8 shown, the steps 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 battery module where the battery is located may include:

[0183] S213. Determine 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. Among them, the noise correlation coefficient represents the noise deviation degree between the battery and the adjacent battery.

[0184] 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. Among them, the low-frequency noise test data of the adjacent battery of the battery and the battery can be divided to obtain the noise correlation coefficient between the adjacent battery of the battery and the battery.

[0185] However, in the embodiments of the present application, the low-frequency noise test data between the battery and the adjacent battery of the battery is subtracted to obtain the noise correlation coefficient between the battery and the adjacent battery of the battery.

[0186] S214. Determine the noise correlation coefficient as the noise evaluation value of the target state index.

[0187] Among them, 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. Optionally, the above noise correlation coefficient can be equal to any value within the interval [0, 1].

[0188] In the battery state detection method according to the embodiments of the present application, the noise correlation coefficient between the battery and its adjacent battery can be determined based on 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 as the noise evaluation value of the target state index. As a result, this method can not only determine the noise evaluation value of the target state index by taking the average value, but also determine the noise evaluation value of the target state index by calculating the noise correlation coefficient between the battery and its adjacent battery, further increasing the determination methods of the noise evaluation value of the target state index and making the determination method of the noise evaluation value of the target state index more flexible. At the same time, when determining the noise evaluation value of the target state index, this method does not need to consider the test data of other batteries in the module, thereby improving the accuracy of the noise evaluation value.

[0189] In order to improve the accuracy of the detection result of the battery under the target state index, the noise evaluation value corresponding to the battery under the target critical state can be obtained first, and then the detection result of the battery under the target state index can be determined according to the noise evaluation value of the battery under the target state index and the noise critical condition. In one embodiment, as Figure 9 shown, the step of 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 S220 above may include:

[0190] S221. Obtain the noise critical condition corresponding to the battery under the target critical state.

[0191] Specifically, the above target critical state may be the critical state corresponding to the battery during the manufacturing stage or the critical state corresponding to the battery during the use stage. It should be noted here that if the target state index is the manufacturing defect degree (such as the voltage drop of the battery per unit time, that is, the K value), the target critical state can be understood as the state when a normal battery just becomes a defective battery during the manufacturing stage; if the target state index is the self-discharge degree, the target critical state can be understood as the state when a normal battery just becomes a battery with a high self-discharge degree during the use stage; if the target state index is the aging degree, the target critical state can be understood as the state when a normal battery just becomes a battery with a large aging degree during the use stage; if the target state index is the lithium plating degree, the target critical state can be understood as the state when a normal battery just becomes a battery with a large lithium plating degree during the use stage; if the target state index is the internal short circuit degree, the target critical state can be understood as the state when a normal battery just becomes a battery with a large internal short circuit degree during the use stage. It can also be understood here that the target state index and the target critical state are in one-to-one correspondence.

[0192] Among them, obtaining the noise critical condition corresponding to the battery in the target critical state can be understood as obtaining the noise evaluation value corresponding to the battery in the target critical state. In practical applications, the noise critical condition corresponding to the battery in the target critical state can be obtained through a large number of experiments. For example, a large number of normal batteries of the corresponding system can be tested under a certain noise test condition to obtain the noise evaluation values of the normal batteries, and the noise evaluation values of the normal batteries can be determined as the noise critical conditions of the batteries of the corresponding system in the target critical state. Another example is that based on the noise critical conditions of the normal batteries of the corresponding system obtained from historical experiments, equation fitting can be performed on the noise critical conditions determined by the historical experiments, and the unknown parameters or ranges of unknown parameters in the fitting equation can be determined through an estimation algorithm. Furthermore, the noise critical condition of the battery in the target critical state can be determined according to the unknown parameters or ranges of unknown parameters. Optionally, the above noise critical condition can be a noise critical threshold, or a noise critical threshold range (that is, including an upper noise critical threshold and a lower noise critical threshold). Moreover, the noise critical conditions corresponding to different critical states can be the same or different.

[0193] Among them, the method of obtaining the noise critical condition corresponding to the battery in the target critical state is the same as the method of obtaining the noise evaluation value of the battery under the target state index in the previous text, and this embodiment of the present application will not elaborate on this.

[0194] S222. Determine the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition.

[0195] Based on the noise evaluation value of the battery under the target state index and the noise critical condition corresponding to the battery in the target critical state obtained in the previous steps, determine the detection result of the battery under the target state index.

[0196] Among them, 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 can be to input both the noise evaluation value of the battery under the target state index and the noise critical condition corresponding to the battery in the target critical state obtained in the previous steps into a pre-trained algorithm model, so as to obtain the detection result of the battery under the target state index.

[0197] 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 can also be to perform a comparison process on 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. Optionally, the comparison result can be that the noise evaluation value does not meet the noise critical condition or the noise evaluation value meets the noise critical condition; the above detection result of the battery under the target state index can be abnormal or normal.

[0198] In addition, in practical applications, in some other scenarios, accurate detection results can be determined based on the noise evaluation value and the noise critical condition. Based on this, in one embodiment, the step of determining the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition in S222 above may include: if the noise evaluation value does not meet the noise critical condition, it is determined that the detection result of the battery under the target state index is abnormal; if the noise evaluation value meets the noise critical condition, it is determined that the detection result of the battery under the target state index is normal.

[0199] In the embodiment of the present application, if it is determined that the noise evaluation value does not meet the noise critical condition, it can be directly determined that the detection result of the battery under the target state index is abnormal; if it is determined that the noise evaluation value meets the noise critical condition, it can be directly determined that the detection result of the battery under the target state index is normal.

[0200] It should be noted here that if the target state index is the manufacturing defect degree, the detection result of the battery under the target state index being abnormal can be understood as that the battery has manufacturing defects; if the target state index is the self-discharge degree, the detection result of the battery under the target state index being abnormal can be understood as that the battery has a defect of high self-discharge degree; if the target state index is the aging degree, the detection result of the battery under the target state index being abnormal can be understood as that the battery has a defect of large aging degree; if the target state index is the lithium deposition degree, the detection result of the battery under the target state index being abnormal can be understood as that the battery has a defect of large lithium deposition degree; if the target state index is the internal short-circuit degree, the detection result of the battery under the target state index being abnormal can be understood as that the battery has a defect of large internal short-circuit degree. Among them, the detection result of the battery under the target state index being normal can be understood as that the battery has no defects and is a battery in a normal state.

[0201] The battery state detection method in the embodiment of the present application can obtain the noise critical condition corresponding to the battery in the target critical state, and determine the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition of the ideal state battery, so that the determined detection result is relatively accurate and improves the accuracy of the detection result of the battery under the target state index.

[0202] In practical applications, the noise critical condition is obtained from the target critical state battery, but it is difficult to produce a standard and reliable target critical state battery. Therefore, the simple, convenient and fast average noise method can be used to determine the noise critical condition. The process of determining the noise critical condition by the average noise method will be described below. In one embodiment, the above noise critical condition is a noise threshold, such as Figure 10 shown, the above battery state detection method may further include:

[0203] S223. Obtain the average value of the low-frequency noise test data 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.

[0204] In the embodiment of the present application, it is also necessary to obtain the low-frequency noise test data of other batteries in the module where the battery is located. Among them, the method of obtaining the low-frequency noise test data of other batteries in the module where the battery is located is similar to the method of obtaining the low-frequency noise test data of the battery in the above step S100, and the embodiment of the present application will not elaborate on this.

[0205] 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 averaged to obtain the average value of the low-frequency noise test between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries.

[0206] S224. Determine the average value of the low-frequency noise test as the noise threshold.

[0207] In the embodiment of the present application, the obtained average value of the low-frequency noise test is directly determined as the noise critical condition, that is, the noise threshold.

[0208] The battery state detection method in the embodiment of the present application, on the basis of obtaining the low-frequency noise test data of the battery, can also obtain the low-frequency noise test data of other batteries in the module where the battery is located, and obtain the average value of the low-frequency noise test between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries, and then determine the average value of the low-frequency noise test as the noise threshold; this process does not require a target critical state battery to determine the noise critical condition, making the process of determining the noise critical condition simple, convenient and fast.

[0209] In some scenarios, it is necessary to classify the level of the abnormal detection result to improve the fineness of the abnormal detection result and provide a basis for selecting effective solutions when solving the abnormality. Based on this, it is necessary to set multiple different noise critical conditions to determine the detection results of different degrees of the battery under the target state index. In one embodiment, the above noise critical conditions include a first threshold, a second threshold and a third threshold; then as Figure 11 shown, the step of determining the detection result of the battery under the target state index according to the detection strategy of the noise evaluation value and the target state index in the above S220 may include:

[0210] S225. If the noise evaluation value exceeds the first threshold and does not exceed the second threshold, it is determined that the detection result of the battery under the target state index is suspected of being abnormal.

[0211] To classify the anomaly detection results, the noise critical condition is the noise critical threshold, which can include multiple different levels of noise critical thresholds. In the embodiments of the present application, taking the noise critical condition including three different levels of noise critical thresholds as an example, the determination of different degrees of anomaly detection results of the battery under the target state index will be described. Among them, the noise critical condition can include a first threshold, a second threshold, and a third threshold. It should be noted here that the first threshold represents the noise critical threshold with a relatively low anomaly level of the battery, the second threshold represents the noise critical threshold with a medium anomaly level of the battery, and the third threshold represents the noise critical threshold with a relatively high anomaly level of the battery. In the embodiments of the present application, the first threshold is less than the second threshold and the second threshold is less than the third threshold.

[0212] After obtaining the noise critical threshold of the battery under the target critical state, the noise critical threshold can be adjusted down to obtain the first threshold, the noise critical threshold can be determined as the second threshold, and the noise critical threshold can be adjusted up to obtain the third threshold. It is also possible to adjust the noise critical threshold according to a preset adjustment rule to obtain the first threshold, the second threshold, and the third threshold respectively.

[0213] Specifically, it can be determined whether the noise evaluation value of the target state index is greater than the first threshold and less than the second threshold. If it is determined that the noise evaluation value of the target state index is greater than the first threshold (i.e., the noise evaluation value exceeds the first threshold) and the noise evaluation value of the target state index 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 index is suspected of being abnormal.

[0214] S226. If the noise evaluation value exceeds the second threshold and does not exceed the third threshold, it is determined that the detection result of the battery under the target state index is slightly abnormal.

[0215] At the same time, it can be determined whether the noise evaluation value of the target state index is greater than the second threshold and less than the third threshold. 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.

[0216] S227. If the noise evaluation value exceeds the third threshold, it is determined that the detection result of the battery under the target state index is severely abnormal.

[0217] Moreover, it can be determined whether the noise evaluation value of the target state index is greater than the third threshold. If it is determined that the noise evaluation value of the target state index is greater 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 severely abnormal.

[0218] In an embodiment of the present application, three different levels of anomaly detection results, namely suspected anomaly, minor anomaly, and severe anomaly, can be determined according to the first threshold, the second threshold, and the third threshold. However, in the processing, it is not limited to determining three different levels of anomaly detection results through three different level thresholds. To improve the accuracy of the determined anomaly detection results, multiple different level thresholds can also be set to determine multiple different levels of anomaly detection results.

[0219] In addition, in an embodiment of the present application, if the target status indicator is the degree of aging, the computer device can directly determine the low-frequency noise test data of the battery as the noise evaluation value of the target status indicator, and then compare the noise evaluation value with the first threshold, the second threshold, and the third threshold respectively to obtain the detection result of the battery under the degree of aging. Optionally, the first threshold, the second threshold, and the third threshold can be the noise evaluation values corresponding to different aging degrees of the battery respectively.

[0220] The following is an example to illustrate the process of determining the detection result of the battery under the degree of aging according to the noise evaluation value and multiple noise thresholds corresponding to the degree of aging. Exemplarily, at a certain voltage, low-frequency noise tests can be pre-conducted on batteries with different aging degrees, such as batteries with aging degrees of 100%, 90%, 85%, 80%, 75%, and 70%, to obtain the low-frequency noise test data of the batteries with different aging degrees. Based on this, when determining the detection results of each battery in a module under the degree of aging, when determining the aging degree of each battery to be tested in the module, for each battery to be tested, a low-frequency noise test is conducted on the battery to be tested. If the obtained low-frequency noise test data is between the low-frequency noise test data corresponding to the batteries with aging degrees of 85% and 80% determined in advance, then the aging degree of the battery to be tested is between 85% and 80%. Further, the detection result of the battery under the degree of aging is determined according to the aging degree interval in which the battery to be tested falls. Here, the low-frequency noise test data of the battery to be tested is the noise evaluation value, and the low-frequency noise test data corresponding to the batteries with aging degrees of 85% and 80% can be two noise thresholds corresponding to the degree of aging.

[0221] Since the previous example requires low-frequency noise tests on batteries with different degrees of aging in advance to obtain the basis for judging the aging degree of different batteries, there may be differences in the same type of battery produced in different batches, and such differences may be amplified during the aging process of the battery. Therefore, the basis for judgment obtained from testing a batch of batteries cannot be applied to all batches of batteries, otherwise the detection accuracy will be severely reduced. At the same time, if only a few batteries with different degrees of aging are selected, such as batteries with aging degrees of 100%, 90%, 85%, 80%, 75%, and 70% respectively, the deviation of the estimated aging degree will necessarily be greater than or equal to 5%. In practical applications, such detection accuracy may not meet the actual requirements. If a large number of batteries with relatively close aging degrees are selected for low-frequency noise tests to determine the basis for judging the aging degree of different batteries, the cost will be relatively high and the process will be relatively complex. In this way, the accuracy problem of the detection results of the battery states of different production batches still cannot be solved. In addition, in practical applications, it is not necessary to clarify the specific aging degree of each battery, and only abnormal batteries with aging degrees significantly different from other batteries need to be detected. Since there are multiple problems as mentioned above in determining the detection results of batteries at the aging degree using the previous example, to solve these problems, the embodiments of the present application can also adopt the following method to determine the detection results of batteries at the aging degree.

[0222] Exemplarily, a low-frequency noise test can be performed on the battery under test to obtain the low-frequency noise test data of the battery under test, and the average value of the low-frequency noise test between the low-frequency noise test data of the battery under test and the low-frequency noise test data of other batteries in the module where the battery under test is located can be obtained and determined as the noise threshold. Then, the difference between the low-frequency noise test data of each battery under test and the noise threshold is compared. If the difference between the low-frequency noise test data and the noise threshold is significantly greater than the difference between the low-frequency noise test data of other batteries and the noise threshold, it is determined that the aging degree of the battery under test is higher. Further, the detection results of the battery at the aging degree can be determined according to the approximate aging degree of the battery under test.

[0223] Since the previous example requires calculating the average value of the low-frequency noise tests of all batteries in the module to determine the noise threshold, the calculation amount is relatively large, and the low-frequency noise test data of abnormal batteries will also be included in the calculation of the noise threshold, thus introducing unnecessary errors. To solve this problem, the embodiments of the present application can also adopt the following method to determine the detection results of batteries at the aging degree.

[0224] Exemplarily, the noise correlation coefficient of the battery to be measured can be calculated and compared with a preset noise threshold. If the noise correlation coefficient is greater than the noise threshold, the greater the corresponding noise correlation coefficient (i.e., the greater the noise evaluation value), and the closer the noise correlation coefficient is to 1, it indicates that there must be a battery with a higher aging degree in the battery to be measured and the adjacent battery of the battery to be measured. Further, in order to determine the battery with a 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 of the battery to be measured and their corresponding adjacent batteries can be calculated respectively, and the battery with a higher aging degree in the battery to be measured and the adjacent battery of the battery to be measured can be located based on the three calculated noise correlation coefficients.

[0225] In the embodiments of the present application, the above steps S225, step S226 and step S227 can be executed synchronously or asynchronously. When executed asynchronously, steps S225, step S226 and step S227 can be executed in any order, and no limitation is imposed on this execution order. Among them, Figure 11 It is shown by the sequential execution of steps S225, step S226 and step S227.

[0226] In practical applications, it can also be first determined whether the noise evaluation value is greater than the first threshold. If the noise evaluation value is greater than the first threshold, it is determined that the current battery is an abnormal battery, and then the steps in the above steps S223, step S226 and step S227 are continued to be executed.

[0227] The battery state detection method in the embodiments of the present application can determine different degrees of abnormal detection results of the battery under the target state index by setting multiple different level thresholds, so that the level of the determined abnormal detection result is more refined, and provide an accurate basis for selecting effective solutions when solving the abnormality.

[0228] In practical applications, after it is determined 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 above steps are executed, as Figure 12 shown, the above battery state detection method further includes the following steps:

[0229] S300. Obtain the backend processing strategy corresponding to the target state index.

[0230] Among them, the backend processing strategy can be a processing strategy pre-planned according to the target state index, and this backend processing strategy can be stored locally, in the cloud or on the hard disk. It should be noted here that the backend processing strategy is a processing method for solving the abnormality of the target state index of the battery. Optionally, the abnormality of the target state index can be understood as that the detection result of the battery under the target state index is abnormal.

[0231] In practical applications, the backend processing strategy corresponding to the target status indicator can be obtained from locations such as local, cloud, or hard disk. Optionally, the backend processing strategies corresponding to different status indicators can be the same or different. In the embodiments of this application, the backend processing strategy can also be understood as a battery management strategy, that is, the strategy of the Battery Management System (BMS) for power batteries.

[0232] S400. If the detection result of the battery under the target status indicator is abnormal, the battery is processed according to the backend processing strategy corresponding to the target status indicator.

[0233] When it is determined that the detection result of the battery under the target status indicator is abnormal, the battery can be processed based on the obtained backend processing strategy corresponding to the target status indicator. Optionally, the backend processing strategy can include repair methods, replacement information, etc.

[0234] After the detection result is determined, mainly corresponding processing is performed on batteries with different degrees of abnormality to improve the safety of the batteries. The following describes the process of processing the battery according to the backend processing strategy corresponding to the target status indicator. In one embodiment, the abnormality includes suspected abnormality, minor abnormality, and serious abnormality; the steps of processing the battery according to the backend processing strategy corresponding to the target status indicator in S400 above can include: if the detection result of the battery under the target status indicator is a suspected abnormality, the detection result of the battery under the target status indicator is determined through a secondary confirmation method; if the detection result of the battery under the target status indicator is a minor abnormality, the battery is repaired through a repair operation; if the detection result of the battery under the target status indicator is a serious abnormality, the battery is scrapped through a scrapping operation.

[0235] Among them, when it is determined that the detection result of the battery under the target status indicator is a suspected abnormality, the battery can be reconfirmed through a secondary confirmation method to obtain a more accurate detection result. At the same time, when it is determined that the detection result of the battery under the target status indicator is a minor abnormality, the battery can be repaired through a repair operation, and when it is determined that the detection result of the battery under the target status indicator is a serious abnormality, the battery can be scrapped through a scrapping operation.

[0236] In the embodiments of the present application, the backend processing strategies corresponding to different degrees of anomaly detection results are not limited to the backend processing strategies described above, and other backend processing strategies may also be used. Exemplarily, if the target status indicator is the degree of internal short circuit, and the noise evaluation value exceeds the first threshold and does not exceed the second threshold, it may be determined that the detection result is a minor anomaly, that is, the degree of internal short circuit is low. At this time, the battery may be reconfirmed through a secondary confirmation method; if the noise evaluation value exceeds the second threshold and does not exceed the third threshold, it is determined that the detection result is a moderate anomaly, that is, the degree of internal short circuit is high. At this time, the battery or the entire module where the battery is located may be replaced; if the noise evaluation value exceeds the third threshold, it is determined that the detection result is a high anomaly, that is, the degree of internal short circuit is extremely high. At this time, a reminder message may be output to notify the user to stay away from the battery and take protection and fire prevention measures.

[0237] If the target status indicator is the degree of lithium plating, and the noise evaluation value exceeds the first threshold and does not exceed the second threshold, it may be determined that the detection result is a minor anomaly, that is, the degree of lithium plating is low. At this time, the battery may be reconfirmed through a secondary confirmation method; if the noise evaluation value exceeds the second threshold and does not exceed the third threshold, it is determined that the detection result is a moderate anomaly, that is, the degree of lithium plating is high. At this time, a reminder message may be output to notify the user that there is a safety risk with the battery and that the battery or the entire module where the battery is located needs to be replaced; if the noise evaluation value exceeds the third threshold, it is determined that the detection result is a high anomaly, that is, the degree of lithium plating is extremely high. At this time, a reminder message may also be output to notify the user that there is a high risk of dendrites piercing the diaphragm and causing an internal short circuit and a safety accident with the battery, and to ask the user to stay away from the battery and take protection and fire prevention measures.

[0238] The embodiments of the present application may adopt different backend processing strategies to correspondingly process abnormal batteries of different degrees in order to prevent safety accidents in a timely manner and also improve the user experience.

[0239] In the embodiments of the present application, when it is initially determined that the detection result is a suspected anomaly, the detection result of the battery under the target status indicator may be reconfirmed to ensure the accuracy of the detection result, thereby avoiding the problem of misprocessing normal batteries by adopting backend processing strategies, saving resources, and also avoiding the problem that abnormal batteries are not processed in a timely manner by adopting backend processing strategies, resulting in low battery safety; at the same time, this method can adopt corresponding backend processing strategies according to different levels of anomaly detection results to correspondingly process the battery, which can not only prevent inferior batteries from flowing into the market and causing safety accidents, but also prevent abnormal batteries from being used normally during the use stage and causing customer complaint incidents, further improving the safety of the battery and increasing the user experience.

[0240] The process of obtaining the low-frequency noise test data of the battery described above will be explained below. In one embodiment, the steps in S100 can be implemented in the following manner: Obtain the fluctuation data of the voltage or current over time during the discharge of the battery at a specific current through a pre-established low-frequency noise test system, thereby obtaining the low-frequency noise test data; alternatively, detect the fluctuation data of the voltage or current over time of the battery through an integrated test chip to obtain the low-frequency noise test data.

[0241] In the embodiments of the present application, two different detection methods can be used to detect the fluctuation data of the voltage or current over time during the discharge of the battery at a specific current, that is, the low-frequency noise test data of the battery, and the fluctuation data obtained by the two methods is the same.

[0242] Among them, the above-mentioned low-frequency noise test system is as Figure 1 shown. The low-frequency noise test system includes a variable resistor, a low-frequency noise test device, and an amplifier. The connection relationship among the variable resistor, the low-frequency noise test device, and the amplifier is as Figure 1 shown. The variable resistor is connected in parallel with the amplifier, and the amplifier is connected in parallel with the low-frequency noise test device. And during the test, the two ends of the variable resistor are respectively connected to the positive and negative electrodes of the battery. It should be noted here that the low-frequency noise test system fixes different discharge voltages or different discharge currents of the battery during the low-frequency noise test by adjusting the resistance value of the variable resistor, amplifies the discharge voltage or discharge current output by the battery through the amplifier, and then detects the fluctuation data of the amplified discharge voltage or discharge current over time through the low-frequency noise test device, that is, the low-frequency noise test data of the battery is obtained. Further, the low-frequency noise test device can send the detected low-frequency noise test data of the battery to a computer device.

[0243] In the embodiments of the present application, the fluctuation data of the voltage or current over time of the battery can also be detected through an integrated test chip, that is, the low-frequency noise test data of the battery is obtained. Further, the integrated test chip can send the detected low-frequency noise test data of the battery to a computer device. Among them, the detection effect of the integrated test chip is the same as that of the low-frequency noise test system. Only integrating the structure in the low-frequency noise test system on the test chip will make the volume of the integrated test chip smaller, achieving the effect of convenient detection.

[0244] In some scenarios, it is necessary to detect the battery status of all batteries in a module. Therefore, it is necessary to set the initial parameters of each battery in the module to be the same, so that the battery status of all batteries in the same module can be detected more accurately. Based on this, in one embodiment, before performing the steps in S100 above, the battery status detection method may further include the following steps: detecting whether the voltage of the battery meets the voltage consistency requirement; if it meets, performing the step of obtaining the low-frequency noise test data of the battery; if it does not meet, adjusting the battery to meet the voltage consistency requirement through a charge and discharge device.

[0245] When detecting the battery status of all batteries in the same module, the battery power of each battery in the same module can be set to be the same. However, in the embodiments of the present application, since the battery charging speed is slow, in order to shorten the duration of the detection process, the voltage of each battery in the same module can be set to be the same voltage to quickly complete the detection process.

[0246] Specifically, the computer device can detect the voltage of each battery in the same module by controlling a voltage detector, and then compare whether the voltages of two batteries are the same to determine whether the voltages of each battery in the same module meet the voltage consistency requirement. If the voltages of each battery in the same module are equal, it is determined that the voltages of each battery in the same module meet the voltage consistency requirement; if the voltages of each battery in the same module are not equal, it is determined that the voltages of each battery in the same module do not meet the voltage consistency requirement. At this time, the charge and discharge device can be controlled to adjust each battery in the same module to an equal voltage, so that each battery in the module meets the state of voltage consistency requirement.

[0247] Among them, the above charge and discharge device is a charge and discharge device corresponding to the type of battery in the module. Exemplarily, if the type of battery in the module is a lithium battery, the charge and discharge device can be a lithium battery charge and discharge device, such as a square lithium battery charge and discharge device, a soft-pack lithium battery charge and discharge device, a cylindrical lithium battery charge and discharge device, etc.; if the type of battery in the module is a lead-acid battery, the charge and discharge device can be a lead-acid battery charge and discharge device, such as a thyristor charge and discharge device, a bus-type charge and discharge device, a bus-type grid-connected charge and discharge device, etc.

[0248] The battery status detection method in the embodiments of the present application can obtain the low-frequency noise test data of the battery through various methods, which improves the flexibility of obtaining the low-frequency noise test data of the battery.

[0249] In one embodiment, the present application further provides a battery status detection method, which includes the following steps:

[0250] (1) Detect whether the voltage of the battery meets the voltage consistency requirement.

[0251] (2) If the condition is satisfied, perform the step of obtaining the low-frequency noise test data of the battery; if not, adjust the battery to meet the voltage consistency requirement through a charge and discharge device.

[0252] (3) Through a pre-built low-frequency noise test system, obtain the fluctuation data of the voltage or current of the battery over time during discharge at a specific current to obtain the low-frequency noise test data; alternatively, detect the fluctuation data of the voltage or current of the battery over time through an integrated test chip to obtain the low-frequency noise test data.

[0253] (4) According to the low-frequency noise test data, obtain the noise evaluation value of the target state index; the noise evaluation value characterizes the noise level of the health state of the battery under the target state index.

[0254] Among them, the process in step (4) can be implemented in the following three ways:

[0255] The first way, the process in step (4) includes the following steps:

[0256] (41) Perform frequency domain conversion processing on the low-frequency noise test data to obtain the noise power spectrum data of the battery;

[0257] (42) Determine the noise evaluation value of the target state index according to the noise power spectrum data of the battery.

[0258] Among them, the process in step (42) can be implemented in two ways:

[0259] The first way, the noise power spectrum data includes the power spectral density frequency curve under a single voltage or a single current; then the process in step (42) includes at least one of the following ways:

[0260] (421) According to the power spectral density frequency curve, determine the power spectral density value at the specified frequency as the noise evaluation value of the target state index;

[0261] (422) According to the power spectral density frequency curve, determine the amplitude of the power spectral density values within the first preset frequency range as the noise evaluation value of the target state index;

[0262] (423) Determine the turning frequency of the power spectral density frequency curve as the noise evaluation value of the target state index;

[0263] (424) Determine the slope of the power spectral density frequency curve within the second preset frequency range as the noise evaluation value of the target state index.

[0264] The second way, the noise power spectrum data includes multiple power spectral density frequency curves under multiple different voltages or multiple different currents; then the process in step (42) includes at least one of the following ways:

[0265] (425) Determine the noise evaluation value of the target state index according to the variation amount between the power spectral density values at a specified frequency among multiple power spectral density frequency curves;

[0266] (426) Determine the noise evaluation value of the target state index according to the variation amount between the amplitudes of the power spectral density values within a third preset frequency range among multiple power spectral density frequency curves;

[0267] (427) Determine the noise evaluation value of the target state index according to the variation amount between the break frequencies of multiple power spectral density frequency curves;

[0268] (428) Determine the noise evaluation value of the target state index according to the variation amount between the slopes within a fourth preset frequency range among multiple power spectral density frequency curves.

[0269] Second, the process in step (4) includes the following steps:

[0270] (43) Determine the low-frequency noise test data as the noise evaluation value of the target state index.

[0271] Third, the process in step (4) includes the following steps:

[0272] (45) Determine the noise correlation coefficient between the battery and the adjacent battery according to the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries; the noise correlation coefficient represents the noise deviation degree between the battery and the adjacent battery;

[0273] (46) Determine the noise correlation coefficient as the noise evaluation value of the target state index.

[0274] (5) Obtain the noise critical condition corresponding to the battery in the target critical state; or, if the noise critical condition is a noise threshold, obtain 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 in the module where the battery is located, and determine the low-frequency noise test average value as the noise threshold.

[0275] (6) The noise critical condition includes a first threshold, a second threshold, and a third threshold; if the noise evaluation value exceeds the first threshold and does not exceed the second threshold, determine that the detection result of the battery under the target state index is suspected of being abnormal, if the noise evaluation value exceeds the second threshold and does not exceed the third threshold, determine that the detection result of the battery under the target state index is slightly abnormal, if the noise evaluation value exceeds the third threshold, determine that the detection result of the battery under the target state index is severely abnormal; or, if the noise evaluation value is less than the noise threshold, determine that the detection result of the battery under the target state index is abnormal, if the noise evaluation value is less than the noise threshold, determine that the detection result of the battery under the target state index is normal.

[0276] (7) Obtain the backend processing policy corresponding to the target status indicator.

[0277] (8) The anomalies include suspected anomalies, minor anomalies, and severe anomalies; if the detection result of the battery under the target status indicator is a suspected anomaly, the detection result of the battery under the target status indicator is determined through secondary confirmation. If the detection result of the battery under the target status indicator is a minor anomaly, the battery is repaired through a repair operation. If the detection result of the battery under the target status indicator is a severe anomaly, the battery is scrapped through a scrapping operation.

[0278] The execution processes of the above (1) to (8) can specifically refer to the descriptions of the above embodiments, and their implementation principles and technical effects are similar, so they will not be elaborated here.

[0279] It should be understood that although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0280] Based on the same inventive concept, the embodiments of the present application also provide a battery status detection device for implementing the battery status detection method involved above. The implementation solution provided by this device to solve problems is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the battery status detection device provided below can refer to the limitations on the battery status detection method in the above text, and will not be elaborated here.

[0281] In one embodiment, Figure 13 is a schematic structural diagram of a battery status detection device in an embodiment of the present application. The battery status detection device provided by the embodiment of the present application can be applied to a computer device. As Figure 13 shown, the battery status detection device of the embodiment of the present application may include: a test data acquisition module 11 and a detection result determination module 12.

[0282] Among them, the test data acquisition module 11 is used to acquire the low-frequency noise test data of the battery;

[0283] The detection result determination module 12 is configured to determine the 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; the target state index includes multiple different battery state indexes.

[0284] The battery state detection device provided by the embodiment of the present application can be used to execute the technical solution in the embodiment of the above battery state detection method of the present application. The implementation principle and technical effect are similar, and will not be described herein again.

[0285] In one embodiment, the detection result determination module 12 includes: a noise evaluation value acquisition unit and a detection result determination unit, where:

[0286] The noise evaluation value acquisition unit is configured to acquire the noise evaluation value of the target state index according to the low-frequency noise test data; the noise evaluation value characterizes the noise level of the health state of the battery under the target state index.

[0287] The detection result determination unit is configured to determine 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.

[0288] The battery state detection device provided by the embodiment of the present application can be used to execute the technical solution in the embodiment of the above battery state detection method of the present application. The implementation principle and technical effect are similar, and will not be described herein again.

[0289] In one embodiment, the noise evaluation value acquisition unit includes: a frequency domain conversion processing subunit and a first determination subunit, where:

[0290] The frequency domain conversion processing subunit is configured to perform frequency domain conversion processing on the low-frequency noise test data to obtain the noise power spectrum data of the battery.

[0291] The first determination subunit is configured to determine the noise evaluation value of the target state index according to the noise power spectrum data of the battery.

[0292] The battery state detection device provided by the embodiment of the present application can be used to execute the technical solution in the embodiment of the above battery state detection method of the present application. The implementation principle and technical effect are similar, and will not be described herein again.

[0293] In one embodiment, the noise power spectrum data includes a power spectral density frequency curve under a single voltage or a single current; the first determination subunit is used for:

[0294] Determine the power spectral density value at the specified frequency as the noise evaluation value of the target state index according to the power spectral density frequency curve; [[ID=,34]]

[0295] Determine the amplitude of the power spectral density value within the first preset frequency range as the noise evaluation value of the target state index according to the power spectral density frequency curve;

[0296] Determine the turning frequency of the power spectral density frequency curve as the noise evaluation value of the target state index;

[0297] Determine the slope of the power spectral density frequency curve within the second preset frequency range as the noise evaluation value of the target state index.

[0298] The battery state detection device provided by the embodiments of the present application can be used to execute the technical solutions in the above embodiments of the battery state detection method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0299] In one embodiment, the noise power spectrum data includes multiple power spectral density frequency curves under multiple different voltages or multiple different currents; the first determination subunit is further configured to:

[0300] Determine the noise evaluation value of the target state index according to the change amount between the power spectral density values at the specified frequency among the multiple power spectral density frequency curves;

[0301] Determine the noise evaluation value of the target state index according to the change amount between the amplitudes of the power spectral density values within the third preset frequency range among the multiple power spectral density frequency curves;

[0302] Determine the noise evaluation value of the target state index according to the change amount between the turning frequencies of the multiple power spectral density frequency curves;

[0303] Determine the noise evaluation value of the target state index according to the change amount between the slopes within the fourth preset frequency range among the multiple power spectral density frequency curves.

[0304] The battery state detection device provided by the embodiments of the present application can be used to execute the technical solutions in the above embodiments of the battery state detection method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0305] In one embodiment, the noise evaluation value acquisition unit includes: a first determination subunit or a second determination subunit, where:

[0306] The first determination subunit is configured to determine the low-frequency noise test data as the noise evaluation value of the target state index;

[0307] The second determination subunit is configured to determine 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.

[0308] The battery state detection device provided by the embodiments of the present application can be used to execute the technical solutions in the above embodiments of the battery state detection method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0309] In one embodiment, the second determination subunit is specifically configured to:

[0310] Determine a noise correlation coefficient between the battery and an 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; the noise correlation coefficient represents the degree of noise deviation between the battery and the adjacent battery;

[0311] Determine the noise correlation coefficient as the noise evaluation value of the target state index.

[0312] The battery state detection device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the battery state detection method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0313] In one embodiment, the detection result determination unit includes: a critical condition acquisition subunit and a detection result determination subunit, where:

[0314] The critical condition acquisition subunit is configured to acquire a noise critical condition corresponding to the battery under the target state index;

[0315] The detection result determination subunit is configured to determine the detection result of the battery under the target state index according to the noise evaluation value and the noise critical condition.

[0316] The battery state detection device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the battery state detection method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0317] In one embodiment, the detection result determination subunit is specifically configured to:

[0318] If the noise evaluation value does not meet the noise critical condition, determine that the detection result of the battery under the target state index is abnormal;

[0319] If the noise evaluation value meets the noise critical condition, determine that the detection result of the battery under the target state index is normal.

[0320] The battery state detection device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the battery state detection method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0321] In one embodiment, the noise critical condition is a noise threshold, and the detection result determination unit further includes: an average value acquisition subunit and a third determination subunit, where:

[0322] The average value acquisition subunit is configured to acquire an average value of low-frequency noise tests between the low-frequency noise test data of the battery and the low-frequency noise test data of other batteries;

[0323] A third determination subunit, configured to determine the average value of the low-frequency noise test as the noise threshold.

[0324] The battery state detection device provided by the embodiment of the present application can be used to execute the technical solutions in the above-mentioned battery state detection method embodiments of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0325] In one embodiment, the noise critical conditions include a first threshold, a second threshold, and a third threshold; the detection result determination subunit is further specifically configured to:

[0326] If the noise evaluation value exceeds the first threshold and does not exceed the second threshold, determine that the detection result of the battery under the target state index is suspected of being abnormal;

[0327] If the noise evaluation value exceeds the second threshold and does not exceed the third threshold, determine that the detection result of the battery under the target state index is slightly abnormal;

[0328] If the noise evaluation value exceeds the third threshold, determine that the detection result of the battery under the target state index is severely abnormal.

[0329] The battery state detection device provided by the embodiment of the present application can be used to execute the technical solutions in the above-mentioned battery state detection method embodiments of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0330] In one embodiment, the battery state detection device further includes: a processing policy acquisition module and an exception handling module, where:

[0331] The processing policy acquisition module is configured to acquire the backend processing policy corresponding to the target state index;

[0332] The exception handling module is configured to process the battery according to the backend processing policy corresponding to the target state index when the detection result of the battery under the target state index is abnormal.

[0333] The battery state detection device provided by the embodiment of the present application can be used to execute the technical solutions in the above-mentioned battery state detection method embodiments of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0334] In one embodiment, the exceptions include suspected exceptions, slight exceptions, and severe exceptions; the exception handling module includes a first processing unit, a second processing unit, and a third processing unit, where:

[0335] The first processing unit is configured to determine the detection result of the battery under the target state index by means of secondary confirmation when the detection result of the battery under the target state index is suspected of being abnormal;

[0336] A second processing unit, configured to repair the battery through a repair operation when the detection result of the battery under the target state index is slightly abnormal;

[0337] A third processing unit, configured to scrap the battery through a scrapping operation when the detection result of the battery under the target state index is severely abnormal.

[0338] The battery state detection device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the battery state detection method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0339] In one embodiment, the test data acquisition module 11 is specifically configured to:

[0340] Obtain the fluctuation data of the voltage or current over time during the discharge of the battery at a specific current through a pre-built low-frequency noise test system, and obtain low-frequency noise test data; or,

[0341] Detect the fluctuation data of the voltage or current of the battery over time through an integrated test chip, and obtain low-frequency noise test data.

[0342] The battery state detection device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the battery state detection method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0343] In one embodiment, the battery state detection device further includes: a consistency detection module, a first determination module, and a second determination module, where:

[0344] The consistency detection module is configured to detect whether the voltage of the battery meets the voltage consistency requirement;

[0345] The first determination module is configured to execute the step of obtaining the low-frequency noise test data of the battery when the detection result of the consistency detection module is satisfied;

[0346] The second determination module is configured to adjust the battery to meet the voltage consistency requirement through a charge and discharge device when the detection result of the consistency detection module is not satisfied.

[0347] The battery state detection device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the battery state detection method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0348] For the specific limitations of the battery state detection device, reference can be made to the limitations of the battery state detection method in the foregoing text, which will not be elaborated herein. Each module in the above battery state detection device can be implemented in whole or in part by software, hardware, and their combinations. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0349] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 14 shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used 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, a computer program, and an information library. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The information library of the computer device is used to store driving feature information. The network interface of the computer device is used to communicate with an external endpoint through a network connection. When the computer program is executed by the processor, it implements a battery state detection method.

[0350] Those skilled in the art can understand that Figure 14 the structure shown in

[0351] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0352] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the technical solution of the above battery state detection method of this application. The implementation principle and technical effect are similar, and will not be elaborated herein.

[0353] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, it implements the technical solution of the above battery state detection method of this application. The implementation principle and technical effect are similar, and will not be elaborated herein.

[0354] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0355] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, not to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and the description of this application. In particular, as long as there is no structural conflict, the technical features mentioned in each embodiment can be combined in any way. This application is not limited to the specific embodiments disclosed in the text, but includes 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: Performing frequency domain conversion processing on the low-frequency noise test data to obtain noise power spectrum data of the battery; 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, characterized in that The noise power spectrum data includes 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 includes 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, characterized in that The obtaining of the noise evaluation value of the target state indicator according to the low-frequency noise test data includes: 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, characterized in that The determining the noise evaluation value of the target state indicator 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 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, characterized in that The determining, based on the noise evaluation value and the detection strategy of the target state indicator, a detection result of the battery under the target state indicator includes: 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, characterized in that 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 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, characterized in that The noise critical condition is a noise threshold, and the method further includes: 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, characterized in that 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, characterized in that The method further comprises: 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, characterized in that The abnormality includes suspected abnormality, minor abnormality and serious abnormality; the processing of the battery according to the back-end processing strategy corresponding to the target state indicator includes: 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, characterized in that The obtaining of low-frequency noise test data of the battery includes: 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 includes: Detecting whether the voltage of the battery meets the voltage consistency requirement; 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: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 12 are implemented.

15. A computer-readable storage medium having a computer program stored thereon, 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.

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.

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