A method and device for diagnosing battery safety status

Abstract

The present invention provides a method and device for diagnosing the safety status of a battery, wherein the method comprises: obtaining battery cell data of each battery cell in a battery to be detected in different time periods; generating battery virtual aging curves corresponding to each battery cell respectively according to the battery cell data of each battery cell in different time periods; generating battery virtual fault curves corresponding to different time periods respectively according to the battery cell data of each battery cell in different time periods; determining the safety status of the battery to be detected according to the battery virtual aging curve and the battery virtual fault curve. By implementing the present invention, the influence of the battery cell electrical signal on the collected data is eliminated, the accuracy of diagnosing the internal resistance of the battery is increased, the safety status of the battery is quickly diagnosed, and the battery cell status can be intuitively judged and battery problems can be quickly handled.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and device for diagnosing a battery safety status. Background Art

[0002] Battery safety is a potential danger in our current life. An abnormal battery may spontaneously combust, suddenly explode, or have abnormal problems such as power failure and leakage, which threaten people's life safety everywhere.

[0003] In the prior art, when the battery internal resistance is used to determine the safety status of the battery, the battery internal resistance is first calculated by real-time acquisition of the voltage and current of the battery cell in a single time period, and then the battery safety status is determined based on the battery internal resistance. However, when the voltage or current of the battery cell fluctuates instantaneously, the signal error of the real-time acquisition is large, which reduces the accuracy of calculating the battery internal resistance, and thus the battery safety status cannot be accurately diagnosed. If the battery resistance value cannot be accurately diagnosed, it will not only interfere with the judgment of whether the battery has been used beyond the expiration date, but also fail to accurately output the resistance value of any group of batteries, which reduces the judgment of battery cell failure and thus cannot correctly judge whether the battery has a fault. Summary of the invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the battery internal resistance diagnosis accuracy defect in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for diagnosing a battery safety status, comprising the following steps: acquiring battery cell data of each cell in a battery to be detected in different time periods; generating battery virtual aging curves corresponding to each cell according to the battery cell data of each cell in different time periods; generating battery cell virtual fault curves corresponding to different time periods according to the battery cell data of each cell in different time periods; and determining the safety status of the battery to be detected according to the battery virtual aging curve and the battery cell virtual fault curve.

[0007] Optionally, in the method for diagnosing the safety status of a battery provided by the present invention, determining the safety status of the battery to be detected according to the battery virtual aging curve and the battery cell virtual failure curve includes:

[0008] Detecting the aging condition of the battery to be detected according to the battery virtual aging curve;

[0009] Determining the safety status of the battery cell of the battery to be tested according to the battery cell virtual fault curve;

[0010] If the battery to be detected is in a non-aging state and the cells of the battery to be detected are safe, it is determined that the battery to be detected is in a safe state.

[0011] Optionally, in the method for diagnosing the battery safety status provided by the present invention, detecting the aging status of the battery to be detected according to the battery virtual aging curve includes:

[0012] If the data growth rate in the battery virtual aging curve is greater than a first preset value, it is determined that the battery to be detected is in an aging state.

[0013] Optionally, in the method for diagnosing the battery safety status provided by the present invention, determining the safety status of the battery cell of the battery to be detected according to the battery cell virtual fault curve includes:

[0014] If the data growth rate in the battery cell virtual failure curve is greater than a second preset value, it is determined that the battery cell to be detected is faulty.

[0015] In a second aspect, the present invention provides a device for diagnosing the safety status of a battery, comprising: a battery data collection module, used to obtain battery cell data of each battery cell in a battery to be detected in different time periods; an aging fault module, used to generate battery virtual aging curves corresponding to each battery cell according to the battery cell data of each battery cell in different time periods; a battery cell failure module, used to generate battery cell virtual failure curves corresponding to different time periods according to the battery cell data of each battery cell in different time periods; and a detection module, used to determine the safety status of the battery to be detected according to the battery virtual aging curve and the battery cell virtual failure curve.

[0016] Optionally, in the device for diagnosing battery safety status provided by the present invention, the aging fault module includes: a battery aging curve submodule, used to generate a corresponding battery virtual aging curve according to the battery cell data; and a battery aging detection submodule, used to detect the aging condition of the battery to be detected according to the battery virtual aging curve.

[0017] Optionally, in the battery safety status diagnosis device provided by the present invention, the cell fault module includes: a cell fault curve submodule, used to generate a corresponding cell virtual fault curve according to the battery cell data; and a cell fault detection submodule, used to detect the safety status of the battery cell to be detected according to the cell virtual fault curve.

[0018] Optionally, in the battery safety status diagnosis device provided by the present invention, when the detection module diagnoses the safety of the television, if the battery to be detected is in a non-aging state and the battery cells of the battery to be detected are safe, the safety diagnosis submodule is used to determine that the battery to be detected is in a safe state.

[0019] In a third aspect, the present invention provides a computer device, comprising:

[0020] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to perform the method for diagnosing the battery safety status as provided in the first aspect of the present invention.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for diagnosing battery safety status as provided in the first aspect of the present invention.

[0022] The technical solution of the present invention has the following advantages:

[0023] The method for diagnosing the safety status of a battery provided by the present invention forms a battery virtual aging curve and a battery cell virtual fault curve through the battery cell data collected in multiple time periods, and diagnoses the safety status of the battery based on the battery virtual aging curve and the battery cell virtual fault curve. Even if the voltage or current collected in one of the time periods fluctuates instantaneously, it will not have a significant impact on the battery virtual aging curve and the battery cell virtual fault curve. Therefore, the method for diagnosing the safety status of a battery provided by an embodiment of the present invention determines whether the battery is in an aging state by diagnosing the battery resistance value through dynamic distribution. If so, the battery service cycle is limited to enhance the safety of battery use. At the same time, the battery cell status is judged. If the battery cell is abnormal, the problem battery is quickly processed, thereby reducing the impact of the battery cell electrical signal error on the diagnosis result, thereby being able to accurately judge the safety status of the battery to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A flowchart of a specific example of a method for diagnosing a battery safety status in an embodiment of the present invention;

[0026] Figure 2 It is a structural schematic diagram of a specific example of a device for diagnosing battery safety status in an embodiment of the present invention;

[0027] Figure 3 It is a structural diagram of a specific example of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] In the description of the present invention, it should be noted that the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as there is no conflict between them.

[0030] Example 1

[0031] This embodiment provides a method for diagnosing battery safety status, which can be applied to battery internal resistance diagnosis, such as Figure 1 As shown, the following steps are included:

[0032] Step S1: Obtaining battery cell data of each battery cell in the battery to be tested in different time periods.

[0033] In an optional embodiment, the battery cell data includes but is not limited to the current and voltage of the battery cell to be detected.

[0034] In an optional embodiment, the types of batteries to be tested include but are not limited to nickel-cadmium batteries, nickel-metal hydride batteries, lithium-ion batteries, lithium polymer batteries, etc.

[0035] In an optional embodiment, the battery to be tested includes multiple battery cells B1, B2, ..., B(n). When diagnosing the battery safety status, one or more data acquisition instructions are first obtained. Different data acquisition instructions correspond to different battery cells. The voltage and current of the battery cells at the diagnosis time △t1, △t2, ..., △t(m) are collected in real time to control the collection of battery cell electrical signal data.

[0036] In an optional embodiment, when abnormal data appears in the battery cell data when the battery cell data is collected, an interference instruction may be used to interfere with the abnormal data to avoid collecting the abnormal data, thereby affecting the judgment of the battery safety status.

[0037] Step S2: generating battery virtual aging curves corresponding to the respective battery cells according to the battery cell data of the respective battery cells in different time periods.

[0038] In an optional embodiment, the battery cell data includes the voltage and current of each battery cell at different times. When generating a battery virtual aging curve, the cell resistance of the battery cell is first calculated based on the voltage and current of the battery cell at different times, and the generated cell resistance is dynamically stored. Then, based on the data required by the battery virtual aging curve, the data stored in the storage domain is dynamically transformed and stored to generate the required data, and then a battery virtual aging curve corresponding to the battery cell is generated based on the data.

[0039] In an optional embodiment, the resistance of the battery cells B1, B2, ..., B(n) in the storage domain at time △t1, △t2, ..., △t(m) is obtained respectively, and the obtained battery cell resistance data is dynamically transformed and stored to generate [R11, R12, ..., R1(m)]1, [R21, R22, ..., R2(m)]2, ..., [R(n)1, R(n)2, ..., R(n)(m)](n) and generate a corresponding battery virtual aging curve.

[0040] In an optional embodiment, each battery cell corresponds to a battery virtual aging curve.

[0041] Step S3: generating cell virtual fault curves corresponding to different time periods respectively according to the battery cell data of each cell in different time periods.

[0042] In an optional embodiment, the cell resistance of each battery cell at different times is first generated according to the battery cell data, and then the cell resistance sets corresponding to different times are generated according to the cell resistances of different battery cells at the same time: the resistances of all battery cells at times △t1, △t2, ..., △t(m) are obtained from the storage domain, and the obtained battery cell resistance data are dynamically transformed and stored to generate [R11, R21, ..., R(n)1]1, [R12, R22, ..., R(n)2]2, ..., [R1(m), R2(m), ..., R( n)(m)](m), wherein [R1(m), R2(m), …, R(n)(m)](m) represents a cell resistance set generated by the cell resistance of the battery cells B1, B2, …, B(n) in the mth time period, and the cell virtual fault curves corresponding to the battery cells at △t1, △t2, …△t(m) are generated respectively according to [R11, R21, …, R(n)1]1, [R12, R22, …, R(n)2]2, …, [R1(m), R2(m), …, R(n)(m)](m).

[0043] In an optional embodiment, each time period corresponds to a battery cell virtual fault curve.

[0044] Step S4: Determine the safety status of the battery to be tested according to the battery virtual aging curve and the battery cell virtual failure curve.

[0045] The method for diagnosing the safety status of a battery provided in an embodiment of the present invention forms a battery virtual aging curve and a battery cell virtual fault curve through the battery cell data collected in multiple time periods, and diagnoses the battery safety status based on the battery virtual aging curve and the battery cell virtual fault curve. Even if the voltage or current collected in one of the time periods fluctuates instantaneously, it will not affect the battery virtual aging curve and the battery cell virtual fault curve. Therefore, the method for diagnosing the safety status of a battery provided in an embodiment of the present invention eliminates the influence of the battery cell electrical signal error on the diagnosis result, thereby being able to accurately judge the safety status of the battery to be tested.

[0046] In an optional embodiment, the safety state of the battery includes a normal state and an abnormal state.

[0047] In an optional embodiment, the abnormal state includes a battery aging state and a battery cell failure state.

[0048] In an optional embodiment, when judging the battery state according to the value in the battery virtual aging curve, the aging state of the battery to be detected is determined by comparing the relevant data in the battery virtual aging curve with the first preset value and the third preset value.

[0049] The first preset value and the third preset value can be set according to actual needs. For example, the first preset value is set to 50%, and the third preset value is set to 20%. When the data growth rate in the battery virtual aging curve is greater than 50%, and the values ​​in the battery virtual aging curve that exceed the safety range account for 20% of the total values, the battery is considered to be in an aged state.

[0050] In an optional embodiment, the battery internal resistance has a normal fluctuation range, that is, the battery can be considered to be in a normal state if the value of the battery internal resistance fluctuates within this range. Even if the battery internal resistance changes within the normal fluctuation range, the data growth rate in the battery virtual aging curve may be greater than the first preset value. Therefore, in order to improve the reliability of the judgment result, a safety range is added as a judgment condition when judging the battery safety state.

[0051] In an optional embodiment, when judging the battery safety status, when the data growth rate in the battery virtual aging curve is less than a first preset value, and the proportion of values ​​outside the safety range to the total values ​​in the curve is greater than a third preset value, the battery to be tested is determined to be in an aging state.

[0052] In an optional embodiment, when judging the battery safety status, when the data growth rate in the battery virtual aging curve is greater than a first preset value, and the proportion of values ​​outside the safety range to the total values ​​in the curve is greater than a third preset value, the battery to be tested is determined to be in an aging state.

[0053] In an optional embodiment, when judging the safety status of the battery, when the data growth rate in the battery virtual aging curve is less than a first preset value, and the proportion of values ​​outside the safety range to the total values ​​in the curve is less than a third preset value, the battery to be tested is determined to be in a normal state.

[0054] In an optional embodiment, when judging the safety status of the battery, when the data growth rate in the battery virtual aging curve is greater than a first preset value, and the proportion of values ​​outside the safety range to the total values ​​in the curve is less than a third preset value, the battery to be tested is determined to be in a normal state.

[0055] In an optional embodiment, when it is determined that the battery to be detected is in an aged state, the battery use safety is enhanced by limiting the battery use cycle.

[0056] In an optional embodiment, when judging the battery state according to the value in the cell virtual fault curve, the safety state of the battery cell to be detected is determined by comparing the relevant data in the cell virtual fault curve with the second preset value and the fourth preset value.

[0057] The second preset value and the fourth preset value can be set according to actual needs. For example, the second preset value is set to 50% and the fourth preset value is set to 20%. When the data growth rate in the virtual fault curve of the battery cell is greater than 50%, and the values ​​in the virtual fault curve of the battery cell that exceed the safety range account for 20% of the total values, the battery cell is considered to be in a fault state.

[0058] In an optional embodiment, the battery internal resistance has a normal fluctuation range, that is, the battery can be considered to be in a normal state if the value of the battery internal resistance fluctuates within this range. Even if the battery internal resistance changes within the normal fluctuation range, the data growth rate in the battery cell virtual fault curve may be greater than the second preset value. Therefore, in order to improve the reliability of the judgment result, a safety range is added as a judgment condition when judging the battery safety state.

[0059] In an optional embodiment, when judging the battery safety status, when the data growth rate in the battery cell virtual fault curve is less than the second preset value, and the proportion of the values ​​outside the safety range to the total values ​​in the curve is greater than the fourth preset value, the battery cell to be tested is determined to be in a fault state.

[0060] In an optional embodiment, when judging the safety status of the battery, when the data growth rate in the battery cell virtual fault curve is greater than the second preset value, and the proportion of the values ​​outside the safety range to the total values ​​in the curve is greater than the fourth preset value, the battery cell to be tested is determined to be in a fault state.

[0061] In an optional embodiment, when judging the battery safety status, when the data growth rate in the battery cell virtual fault curve is less than the second preset value, and the proportion of the values ​​outside the safety range to the total values ​​in the curve is less than the fourth preset value, the battery cell to be tested is determined to be in a normal state.

[0062] In an optional embodiment, when judging the safety status of the battery, when the data growth rate in the virtual fault curve of the battery cell is greater than the second preset value, and the proportion of the values ​​outside the safety range to the total values ​​in the curve is less than the fourth preset value, the battery cell to be tested is determined to be in a normal state.

[0063] In an optional embodiment, when judging the aging state of the battery and the battery cell state according to the battery virtual aging curve and the battery cell virtual fault curve, a safety range is set for its internal resistance. When the curve image of the battery virtual aging curve or the battery cell virtual fault curve fluctuates within the safety range, even if the data growth rate in the battery virtual aging curve is greater than the first preset value or the data growth rate in the battery cell virtual fault curve is greater than the second preset value, the battery aging state or the battery cell state is considered to be normal; when the data growth rate in the battery virtual aging curve is greater than the first preset value or the data growth rate in the battery cell virtual fault curve is greater than the second preset value, and the proportion of the numerical value within the safety range to the total numerical value exceeds the corresponding third preset value or fourth preset value, the battery is considered to be in an aging state or the battery cell state is in an abnormal state.

[0064] Example 2

[0065] This embodiment provides a device for diagnosing battery safety status, such as Figure 2 As shown, including:

[0066] A battery data collection module is used to obtain battery cell data of each battery cell in the battery to be tested in different time periods. The details are described in step S1 in the above embodiment and will not be repeated here.

[0067] An aging fault module is used to generate a battery virtual aging curve corresponding to each battery cell according to the battery cell data of each battery cell in different time periods. The description of step S2 in the above embodiment is omitted here for details.

[0068] A cell fault module is used to generate cell virtual fault curves corresponding to different time periods according to the battery cell data of each cell in different time periods. The description of step S3 in the above embodiment is omitted here for details.

[0069] A detection module, used to determine the safety status of the battery to be detected according to the battery virtual aging curve and the battery cell virtual failure curve. The details of the description of step S4 in the above embodiment will not be repeated here;

[0070] In an optional embodiment, the battery aging curve submodule is used to generate a corresponding battery virtual aging curve according to the battery cell data; the battery aging detection submodule is used to detect the aging condition of the battery to be detected according to the battery virtual aging curve, and the details are described in step S2 in the above embodiment, which will not be repeated here;

[0071] In an optional embodiment, the cell fault curve submodule is used to generate a corresponding cell virtual fault curve according to the battery cell data; the cell fault detection submodule is used to detect the safety status of the battery cell to be detected according to the cell fault curve, and the details are described in step S3 in the above embodiment, which will not be repeated here;

[0072] In an optional embodiment, when the detection module diagnoses the battery safety, if the battery to be detected is in a non-aging state and the battery cell to be detected is safe, the safety diagnosis submodule is used to determine that the battery to be detected is in a safe state. The description of step S4 in the above embodiment is not repeated here.

[0073] Example 3

[0074] An embodiment of the present invention provides a computer device, such as Figure 3 As shown, it includes: at least one processor 31, such as a CPU (Central Processing Unit), at least one communication interface 32, a memory 34, and at least one communication bus 33. The communication bus 33 is used to realize the connection and communication between these components. The communication interface 32 may include a display screen (Display), a keyboard (Keyboard), and the optional communication interface 32 may also include a standard wired interface and a wireless interface.

[0075] The memory 34 can be a high-speed RAM memory (Ramdom Access Memory) or a non-volatile memory, such as at least one disk storage. The memory 34 can optionally be at least one storage device located away from the aforementioned processor 31. The processor 31 can execute the method for diagnosing the battery safety status in Example 1. A set of program codes are stored in the memory 34, and the processor 31 calls the program code stored in the memory 34 to execute the method for diagnosing the battery safety status in Example 1. The communication bus 33 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 33 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one line is used to represent it, but it does not mean that there is only one bus or one type of bus. Among them, the memory 34 may include volatile memory (English: volatile memory), such as random access memory (English: random-access memory, abbreviated: RAM); the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory), hard disk drive (English: hard disk drive, abbreviated: HDD) or solid-state drive (English: solid-state drive, abbreviated: SSD); the memory 34 may also include a combination of the above-mentioned types of memory. Among them, the processor 31 may be a central processing unit (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP. Among them, the processor 31 may further include a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit (English: application-specific integrated circuit, abbreviated: ASIC), a programmable logic device (English: programmable logic device, abbreviated: PLD) or a combination thereof.

[0076] The above-mentioned PLD can be a complex programmable logic device (English: complex programmable logic device, abbreviated: CPLD), a field programmable gate array (English: field-programmable gate array, abbreviated: FPGA), a generic array logic (English: generic array logic, abbreviated: GAL) or any combination thereof.

[0077] Example 4

[0078] An embodiment of the present invention provides a computer-readable storage medium, and the memory is also used to store program instructions. The processor can call the program instructions to implement the method for diagnosing the battery safety status in Example 1 as executed in this application. An embodiment of the present invention also provides a computer-readable storage medium, on which computer executable instructions are stored, and the computer executable instructions can execute the method for diagnosing the battery safety status in Example 1. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0079] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for diagnosing battery safety status, characterized in that: The steps include: Obtain battery cell data of each cell in the battery to be tested at different time periods; Generate a battery virtual aging curve corresponding to each battery cell according to the battery cell data of each battery cell in different time periods; the battery virtual aging curve is generated based on [R11, R12, ..., R1(m)]1, [R21, R22, ..., R2(m)]2, ..., [R(n)1, R(n)2, ..., R(n)(m)](n); [R(n)1, R(n)2, ..., R(n)(m)](n) is a set of battery cell resistance values ​​of battery cell B(n) at time △t1, △t2, ..., △t(m); the battery cell resistance value is calculated based on the battery cell data; the battery cell data includes the voltage and current of each battery cell at different times; Generate cell virtual fault curves corresponding to different time periods according to the battery cell data of each cell in different time periods; the cell virtual fault curves are generated based on [R11, R21, ..., R(n)1]1, [R12, R22, ..., R(n)2]2, ..., [R1(m), R2(m), ..., R(n)(m)](m), and [R1(m), R2(m), ..., R(n)(m)](m) is a set of cell resistance values ​​of battery cells B1, B2, ..., B(n) at time △t(m); The safety state of the battery to be detected is determined according to the battery virtual aging curve and the battery cell virtual failure curve.

2. The method for diagnosing battery safety status according to claim 1, characterized in that: Determining the safety state of the battery to be detected according to the battery virtual aging curve and the battery cell virtual failure curve includes: Detecting the aging condition of the battery to be detected according to the battery virtual aging curve; Determining the safety status of the battery cell of the battery to be tested according to the battery cell virtual fault curve; If the battery to be detected is in a non-aging state and the cells of the battery to be detected are safe, it is determined that the battery to be detected is in a safe state.

3. The method for diagnosing battery safety status according to claim 2, characterized in that: Detecting the aging state of the battery to be detected according to the battery virtual aging curve includes: If the data growth rate in the battery virtual aging curve is greater than a first preset value, it is determined that the battery to be detected is in an aging state.

4. The method for diagnosing battery safety status according to claim 2, characterized in that: Determining the safety status of the battery cell of the battery to be detected according to the battery cell virtual fault curve includes: If the data growth rate in the battery cell virtual failure curve is greater than a second preset value, it is determined that the battery cell to be detected is faulty.

5. A device for diagnosing battery safety status, characterized in that: include: A battery data collection module is used to obtain battery cell data of each cell in the battery to be tested at different time periods; An aging fault module is used to generate a battery virtual aging curve corresponding to each battery cell according to the battery cell data of each battery cell in different time periods; the battery virtual aging curve is generated based on [R11, R12, ..., R1(m)]1, [R21, R22, ..., R2(m)]2, ..., [R(n)1, R(n)2, ..., R(n)(m)](n); [R(n)1, R(n)2, ..., R(n)(m)](n) is a set of battery cell resistance values ​​of battery cell B(n) at time △t1, △t2, ..., △t(m); the battery cell resistance value is calculated based on the battery cell data; the battery cell data includes the voltage and current of each battery cell at different times; A cell fault module is used to generate cell virtual fault curves corresponding to different time periods according to the battery cell data of each cell in different time periods; the cell virtual fault curve is generated based on [R11, R21, ..., R(n)1]1, [R12, R22, ..., R(n)2]2, ..., [R1(m), R2(m), ..., R(n)(m)](m), and [R1(m), R2(m), ..., R(n)(m)](m) is a set of cell resistance values ​​of battery cells B1, B2, ..., B(n) at time △t(m); The detection module is used to determine the safety status of the battery to be detected according to the battery virtual aging curve and the battery cell virtual failure curve.

6. The device for diagnosing battery safety status according to claim 5, characterized in that: The aging fault modules include: A battery aging curve submodule, used to generate a corresponding battery virtual aging curve according to the battery cell data; The battery aging detection submodule is used to detect the aging condition of the battery to be detected according to the battery virtual aging curve.

7. The device for diagnosing battery safety status according to claim 5, characterized in that: The cell fault module includes: A cell fault curve submodule, used to generate a corresponding cell virtual fault curve according to the battery cell data; The battery cell fault detection submodule is used to detect the safety status of the battery cell to be detected according to the battery cell virtual fault curve.

8. The device for diagnosing battery safety status according to claim 5, characterized in that: When the detection module diagnoses the battery safety, if the battery to be detected is in a non-aging state and the battery cells of the battery to be detected are safe, the safety diagnosis submodule is used to determine that the battery to be detected is in a safe state.

9. A computer device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to execute the method for diagnosing the battery safety status as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for diagnosing a battery safety status according to any one of claims 1 to 4.

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