Abnormal battery cell identification method and device, server, and storage medium

Abstract

The application relates to the technical field of battery management, in particular to an abnormal battery cell identification method and device, a server and a storage medium, wherein the method comprises the following steps: acquiring charging data of multiple charging and discharging cycles of each battery cell in a battery pack; identifying charging time and charging current when each battery cell reaches a reference voltage value in the charging data; calculating charging deviation capacity of each battery cell relative to a reference cell of the battery pack according to the charging time and the charging current; fitting charging deviation capacity of each battery cell in each charging and discharging cycle to obtain a curve of charging deviation capacity changing with cycle number, and identifying an abnormal battery cell with self-discharge abnormality in each battery cell according to a fitting slope of the curve. Therefore, the problems that in the prior art, only a small amount of charging data is used for judging the self-discharge abnormality, misjudgment is prone to occur, different charging currents have no influence on charging capacity, and the self-discharge abnormality identification method has a small applicable range are solved.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, specifically to a method, device, server, and storage medium for identifying abnormal battery cells. Background Technology

[0002] With the rapid development and widespread adoption of electric vehicles, the safety of power batteries, as the core component of electric vehicles, has become increasingly important. Accidents such as fires and even explosions caused by thermal runaway of power batteries can easily result in personal injury and property damage, and are currently a key focus and challenge in electric vehicle safety research.

[0003] One of the main causes of thermal runaway in power batteries is an internal short circuit. An internal short circuit creates a circuit within the battery and continuously consumes electricity, manifesting externally as an abnormal decrease in battery charge, i.e., abnormal self-discharge. As the severity of the internal short circuit increases, the abnormal self-discharge of the battery increases, the heat generation increases, and eventually leads to thermal runaway.

[0004] A method and apparatus for detecting micro-short circuits in batteries are disclosed in related technologies. Specifically, the method involves calculating the internal short-circuit leakage current of a battery cell based on the difference in charging capacity between the battery cell and a reference cell at the end of two charging cycles. Further, the internal short-circuit resistance is estimated using the average voltage at the end of the two charging cycles. Finally, the presence of an internal short circuit is determined by comparing the leakage current and internal short-circuit resistance with corresponding threshold values.

[0005] However, this method has the following problems: 1. It only uses two charging processes for judgment, which is easily affected by voltage / current data jumps and missing data when applied to real vehicle data, and may misjudge normal batteries as internal short-circuit batteries; 2. It does not consider the impact of different charging currents on the difference in charging capacity of individual battery cells during the charging process; 3. The calculation process is relatively complex and requires the calculation of multiple parameters such as reference charging time, reference charging capacity, average voltage and internal short-circuit resistance.

[0006] Another related patent discloses a quantitative diagnostic method for internal short circuits in batteries based on the increase in charge within a charging voltage range. By selecting a specific voltage range, the method calculates the growth coefficient of battery charge within that range during two adjacent charging cycles, and compares this growth coefficient with a threshold to determine whether an internal short circuit has occurred. However, this method has the following problems: 1. It only uses the change in charging capacity and whether it exceeds a threshold during two adjacent charging cycles for judgment, which can easily lead to misjudgments when the data changes abruptly; 2. It does not consider the significant difference in charging current between individual battery cells caused by the difference between fast and slow charging modes in real-world vehicle data. Summary of the Invention

[0007] One objective of this invention is to provide a method for identifying abnormal battery cells, in order to solve the problems of existing technologies that rely on only a small amount of charging data to judge self-discharge abnormalities, which is prone to misjudgment and does not take into account the impact of different charging currents on charging capacity, resulting in a limited scope of application for self-discharge abnormality identification methods; a second objective is to provide a device for identifying abnormal battery cells; a third objective is to provide a server; and a fourth objective is to provide a computer-readable storage medium.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] A method for identifying abnormal battery cells, applied to a server, comprising the following steps: acquiring charging data of each battery cell in a battery pack over multiple charge-discharge cycles; identifying the charging time and charging current when each battery cell reaches a reference voltage value in the charging data; calculating the charging deviation capacity of each battery cell relative to a reference cell of the battery pack based on the charging time and the charging current; fitting the charging deviation capacity of each battery cell in each charge-discharge cycle to obtain a curve of charging deviation capacity changing with the number of cycles; and identifying abnormal battery cells with self-discharge abnormalities based on the slope of the fitted curve.

[0010] Based on the above technical means, the embodiments of this application can determine abnormal battery cells with self-discharge abnormalities by linearly fitting the slope of the change in charging deviation capacity within multiple cycles based on the charging deviation capacity of the battery cell compared with the reference cell. This avoids misjudgment caused by data jumps when only two charging processes are used to determine self-discharge abnormalities, and enhances the applicability of battery cell self-discharge abnormality identification.

[0011] Furthermore, the step of identifying abnormal battery cells with self-discharge abnormalities among the battery cells based on the fitting slope of the curve includes: determining whether the fitting slope is greater than a first slope threshold; if the fitting slope is greater than the first slope threshold, then determining that the battery cell is an abnormal battery cell with self-discharge abnormalities.

[0012] Based on the above technical means, the embodiments of this application can directly use the fitting slope and the magnitude of the first slope threshold to determine whether a battery cell is an abnormal battery cell with self-discharge abnormality, without the need for additional complex calculations, thus reducing the amount of calculation.

[0013] Furthermore, the step of identifying abnormal battery cells with self-discharge abnormalities among the battery cells based on the fitting slope of the curve includes: determining whether the fitting slope is less than a second slope threshold; if the fitting slope is less than the second slope threshold, then determining that the reference cell is an abnormal battery cell with self-discharge abnormalities.

[0014] Based on the above technical means, the embodiments of this application can directly use the magnitude of the fitting slope and the second slope threshold to determine whether a battery cell is an abnormal battery cell with self-discharge abnormality, without the need for additional complex calculations, thus reducing the amount of calculation.

[0015] Furthermore, the step of fitting the charging deviation capacity of each battery cell in each charge-discharge cycle to obtain the curve of the charging deviation capacity changing with the number of cycles includes: selecting a charging cycle number window for each charging cell; determining a target cycle number range for each charging cell based on the charging cycle number window; fitting the charging deviation capacity of each charge-discharge cycle within the target cycle number range to obtain the curve of the charging deviation capacity changing with the number of cycles.

[0016] Based on the above technical means, the embodiments of this application can determine the target cycle number range for each charging cell according to the charging cycle number window, fit the charging deviation capacity of each charge-discharge cycle within the target cycle number range, and obtain a curve of the charging deviation capacity changing with the number of charge-discharge cycles, which can be used to determine the subsequent curve based on the fitting slope.

[0017] Furthermore, before fitting the charging deviation capacity of each battery cell in multiple charge-discharge cycles to obtain the curve of the charging deviation capacity changing with the number of cycles, the method further includes: obtaining a correction coefficient of the charging current relative to the reference current of the reference cell; and correcting the charging deviation capacity of each battery cell in each charge-discharge cycle according to the correction coefficient.

[0018] Based on the above technical means, the embodiments of this application can take into account the influence of different charging currents on the charging deviation capacity, calculate and correct the charging deviation capacity, and improve the applicability of the self-discharge anomaly identification method to different charging modes.

[0019] Furthermore, the step of calculating the charging deviation capacity of each battery cell relative to a reference cell of the battery pack based on the charging time and the charging current includes: identifying the charging time of the reference cell; determining the deviation duration based on the charging time of each battery cell and the charging time of the reference cell; and integrating the charging current over time within the deviation duration to obtain the charging deviation capacity of each battery cell relative to the reference cell.

[0020] Based on the above technical means, the embodiments of this application can determine the deviation duration based on the charging time of the battery cell and the charging time of the reference cell, and perform time integration on the charging current within the deviation duration to obtain the charging deviation capacity of the battery cell relative to the reference cell, so as to identify abnormal battery cells with self-discharge abnormalities based on the charging deviation capacity.

[0021] Furthermore, identifying the charging time and charging current when each battery cell in the charging data reaches the reference voltage value includes: taking any battery cell in the battery pack as a reference cell; extracting the voltage change curve of the reference cell in the charging data, and taking the target voltage value in the voltage change curve as the reference voltage value; and obtaining the charging time and charging current when each battery cell reaches the reference voltage value within any voltage range.

[0022] Based on the above technical means, the embodiments of this application can select any battery cell in the battery pack as a reference cell, avoiding additional determination before calculation. Furthermore, the selection of the reference voltage value is not unique; multiple reference voltage values ​​can be selected for calculation to obtain the charging time and charging current of each battery cell when it reaches the reference voltage value within any voltage range, which can be used for subsequent calculation of charging deviation capacity.

[0023] Furthermore, before acquiring the charging data of each battery cell in the battery pack for multiple charge-discharge cycles, the method further includes: acquiring the charge-discharge data of the battery pack; extracting the actual number of charge-discharge cycles from the charge-discharge data; if the actual number of cycles is greater than a preset number, then extracting the charging data for each charge-discharge cycle from the charge-discharge data; otherwise, not identifying abnormal battery cells.

[0024] Based on the above technical means, the embodiments of this application can obtain the charging data of each battery cell in the battery pack during multiple charge-discharge cycles when the number of charge-discharge cycles is greater than a preset number; otherwise, abnormal battery cells are not identified, thus avoiding misjudgment caused by data jumps when only a few charge-discharge processes are used to judge self-discharge abnormalities.

[0025] Furthermore, after identifying abnormal battery cells with self-discharge abnormalities among the battery cells based on the fitting slope of the curve, the method further includes: generating preset reminder information; and sending the preset reminder information to a preset terminal to provide a self-discharge abnormality reminder based on the preset reminder information.

[0026] Based on the above technical means, the embodiments of this application can remind the user after identifying a battery cell with abnormal self-discharge, so as to remind the user to take timely action and avoid thermal runaway of the power battery.

[0027] Furthermore, after identifying abnormal battery cells with self-discharge abnormalities among the battery cells based on the fitting slope of the curve, the method further includes: identifying the identifier of the abnormal battery cell; and sending the identifier to a preset terminal to locate the abnormal battery cell in the battery pack based on the identifier.

[0028] Based on the above technical means, the embodiments of this application can identify the abnormal battery cell after identifying the battery cell with self-discharge abnormality, so that the user can locate the abnormal battery cell in the battery pack according to the identification and replace it in time.

[0029] A device for identifying abnormal battery cells, applied to a server, comprising: an acquisition module for acquiring charging data of each battery cell in a battery pack over multiple charge-discharge cycles; a calculation module for identifying the charging time and charging current of each battery cell when it reaches a reference voltage value in the charging data, and calculating the charging deviation capacity of each battery cell relative to a reference cell of the battery pack based on the charging time and the charging current; and an identification module for fitting the charging deviation capacity of each battery cell in each charge-discharge cycle to obtain a curve of charging deviation capacity changing with the number of cycles, and identifying abnormal battery cells with self-discharge abnormalities based on the slope of the fitted curve.

[0030] Furthermore, the identification module is further used to: determine whether the fitting slope is greater than a first slope threshold; if the fitting slope is greater than the first slope threshold, then determine that the battery cell is an abnormal battery cell with self-discharge abnormality.

[0031] Furthermore, the identification module is further used to: determine whether the fitting slope is less than a second slope threshold; if the fitting slope is less than the second slope threshold, then determine that the reference cell is an abnormal battery cell with self-discharge abnormality.

[0032] Furthermore, the identification module is further configured to: select a charging cycle number window for each charging cell; determine a target cycle number range for each charging cell based on the charging cycle number window; and fit the charging deviation capacity of each charge-discharge cycle within the target cycle number range to obtain a curve of the charging deviation capacity changing with the number of cycles.

[0033] Furthermore, the abnormal battery cell identification device also includes: a correction module, used to obtain a correction coefficient of the charging current relative to the reference current of the reference cell before fitting the charging deviation capacity of each battery cell in multiple charge-discharge cycles to obtain the curve of the charging deviation capacity changing with the number of cycles; and to correct the charging deviation capacity of each battery cell in each charge-discharge cycle according to the correction coefficient.

[0034] Furthermore, the calculation module is further configured to: identify the charging time of the reference cell; determine the deviation duration based on the charging time of each battery cell and the charging time of the reference cell; and perform time integration on the charging current within the deviation duration to obtain the charging deviation capacity of each battery cell relative to the reference cell.

[0035] Furthermore, the calculation module is further configured to: take any single battery cell in the battery pack as a reference cell; extract the voltage change curve of the reference cell from the charging data, and take the target voltage value in the voltage change curve as the reference voltage value; and obtain the charging time and charging current when each battery cell reaches the reference voltage value within any voltage range.

[0036] Furthermore, the abnormal battery cell identification device also includes: an extraction module, used to acquire the charge and discharge data of the battery pack before acquiring the charging data of each battery cell in the battery pack for multiple charge and discharge cycles; extract the actual number of charge and discharge cycles in the charge and discharge data; if the actual number of cycles is greater than a preset number, then extract the charging data of each charge and discharge cycle in the charge and discharge data, otherwise, the abnormal battery cell identification is not performed.

[0037] Furthermore, the abnormal battery cell identification device also includes: an alert module, used to generate preset alert information after identifying abnormal battery cells with self-discharge abnormalities among the battery cells according to the fitting slope of the curve; and to send the preset alert information to a preset terminal to provide a self-discharge abnormality alert based on the preset alert information.

[0038] Furthermore, the abnormal battery cell identification device further includes: a sending module, used to identify the identifier of the abnormal battery cell after identifying the abnormal battery cell with self-discharge abnormality among the battery cells according to the fitting slope of the curve; and send the identifier to a preset terminal to locate the abnormal battery cell in the battery pack according to the identifier.

[0039] A server includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for identifying abnormal battery cells as described in the above embodiments.

[0040] A computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method for identifying abnormal battery cells as described in the above embodiments.

[0041] The beneficial effects of this invention are:

[0042] (1) According to the embodiments of this application, the abnormal battery cell with self-discharge abnormality can be judged by linearly fitting the change slope of the charging deviation capacity of the battery cell compared with the reference cell. This avoids the misjudgment caused by data jump when only two charging processes are used to judge the self-discharge abnormality, and enhances the applicability of battery cell self-discharge abnormality identification.

[0043] (2) The embodiments of this application can directly use the fitting slope and the first slope threshold to determine whether a battery cell is an abnormal battery cell with self-discharge abnormality, without the need for additional complex calculations, thus reducing the amount of calculation.

[0044] (3) The embodiments of this application can directly use the fitting slope and the second slope threshold to determine whether the battery cell is an abnormal battery cell with self-discharge abnormality, without the need for additional complex calculations, thus reducing the amount of calculation.

[0045] (4) In this embodiment of the application, the target cycle number range of each charging cell can be determined according to the charging cycle number window, and the charging deviation capacity of each charge and discharge cycle within the target cycle number range can be fitted to obtain the curve of the charging deviation capacity changing with the number of charge and discharge cycles, which can be used to determine the subsequent curve based on the fitting slope.

[0046] (5) The embodiments of this application can take into account the influence of different charging currents on the charging deviation capacity, calculate and correct the charging deviation capacity, and improve the applicability of the self-discharge abnormality identification method to different charging modes.

[0047] (6) In this embodiment, the deviation duration can be determined based on the charging time of the battery cell and the charging time of the reference cell. The charging current is integrated over time within the deviation duration to obtain the charging deviation capacity of the battery cell relative to the reference cell, so as to identify abnormal battery cells with self-discharge abnormalities based on the charging deviation capacity.

[0048] (7) In this embodiment of the application, any battery cell in the battery pack can be selected as the reference cell, avoiding additional determination before calculation. Furthermore, the selection of the reference voltage value is not unique, and multiple reference voltage values ​​can be selected for calculation to obtain the charging time and charging current of each battery cell when it reaches the reference voltage value in any voltage range, which can be used for subsequent calculation of charging deviation capacity.

[0049] (8) In this embodiment of the application, when the number of charge-discharge cycles is greater than a preset number, the charging data of each battery cell in the battery pack is obtained after multiple charge-discharge cycles. Otherwise, abnormal battery cells are not identified, thus avoiding misjudgment caused by data jumps when only a few charge-discharge processes are used to judge self-discharge abnormalities.

[0050] (9) In this embodiment of the application, after identifying a battery cell with abnormal self-discharge, the user can be reminded to take timely action to avoid thermal runaway of the power battery.

[0051] (10) In this embodiment of the application, after a battery cell with self-discharge abnormality is identified, the abnormal battery cell can be identified so that the user can locate the abnormal battery cell in the battery pack according to the identification and replace it in time.

[0052] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0053] Figure 1 A flowchart illustrating the method for identifying abnormal battery cells provided in an embodiment of the present invention;

[0054] Figure 2 A flowchart illustrating a method for identifying abnormal battery cells according to an embodiment of the present invention;

[0055] Figure 3 A block diagram illustrating the device for identifying abnormal battery cells provided in an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of the server structure provided in an embodiment of the present invention. Detailed Implementation

[0057] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0058] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0059] Specifically, Figure 1 This is a flowchart illustrating a method for identifying abnormal battery cells provided in an embodiment of this application.

[0060] like Figure 1 As shown, the method for identifying this abnormal battery cell includes the following steps:

[0061] In step S101, charging data of each individual battery cell in the battery pack for multiple charge-discharge cycles is obtained.

[0062] This application embodiment can extract the charging data of each battery cell in the battery pack for each charge-discharge cycle based on the long-term operating data of the real vehicle. The real vehicle operating data refers to the voltage curve of each battery cell during the vehicle charging and driving discharge process stored in the cloud.

[0063] In this embodiment of the application, before obtaining the charging data of each battery cell in the battery pack for multiple charge-discharge cycles, the method further includes: obtaining the charge-discharge data of the battery pack; extracting the actual number of charge-discharge cycles from the charge-discharge data; if the actual number of cycles is greater than a preset number, then extracting the charging data of each charge-discharge cycle from the charge-discharge data, otherwise not identifying abnormal battery cells.

[0064] One charge-discharge cycle is defined as one complete charge and discharge cycle of a single battery cell. The preset number of cycles can be set according to specific circumstances and is not limited; for example, it can be set to 6 or 7 cycles.

[0065] It is understood that the embodiments of this application can extract the actual number of times in the charge and discharge data. When the number of charge and discharge cycles is greater than the preset number, the charging data of each battery cell in the battery pack is obtained from multiple charge and discharge cycles. Otherwise, abnormal battery cells are not identified, thus avoiding misjudgment caused by data jumps when only a small number of charge and discharge cycles are used to judge self-discharge abnormalities.

[0066] For example, taking a preset number of times N as 7 times, when the number of charge-discharge cycles is less than N, no self-discharge abnormality judgment is performed. In each charge-discharge cycle, only the charging process data is extracted for the judgment of self-discharge abnormal battery.

[0067] In step S102, the charging time and charging current of each battery cell when it reaches the reference voltage value are identified in the charging data, and the charging deviation capacity of each battery cell relative to the reference cell of the battery pack is calculated based on the charging time and charging current.

[0068] It is understood that, according to the embodiments of this application, the charging deviation capacity of each battery cell relative to the reference cell can be calculated based on the charging time and charging current when each battery cell reaches the reference voltage value.

[0069] The specific calculation process includes: identifying the charging time of the reference cell; determining the deviation duration based on the charging time of each cell and the charging time of the reference cell; and integrating the charging current over time within the deviation duration to obtain the charging deviation capacity of each cell relative to the reference cell.

[0070] The calculation process for the charging deviation capacity relative to the reference cell can be as follows:

[0071]

[0072] in, The charging time when the reference cell reaches the reference voltage in the k-th charging cycle; The charging time when the remaining battery cells reach the reference voltage in the k-th charging cycle, i = 1, 2, ..., n, for a total of n battery cells; This is the charging current of each of the remaining battery cells when they reach the reference voltage value in the kth charging cycle. This represents the charging deviation capacity of each battery cell relative to the reference cell when the reference voltage value is reached in the k-th charging cycle.

[0073] It should be noted that a positive relative charging deviation capacity indicates that the battery cell reaches the reference voltage before the reference cell, and the battery capacity is higher; a negative relative charging deviation capacity indicates that the battery cell reaches the reference voltage after the reference cell, and the battery capacity is lower.

[0074] In this embodiment of the application, identifying the charging time and charging current when each battery cell in the charging data reaches the reference voltage value includes: taking any battery cell in the battery pack as a reference cell; extracting the voltage change curve of the reference cell in the charging data, and taking the target voltage value in the voltage change curve as the reference voltage value; and obtaining the charging time and charging current when each battery cell in any voltage range reaches the reference voltage value.

[0075] The reference cell is any cell randomly selected from the individual cells in the battery pack. The reference voltage is the voltage value that each individual cell can reach under the same charging step current during the actual vehicle charging process. The reference voltage is determined based on the charging voltage curve.

[0076] Due to the stepped charging strategy used in actual vehicle charging, at different voltage ranges [V x V y Different charging currents I were used inside. x The reference voltage value should be within the voltage range [V]. x V y The reference voltage value is selected within a certain range to ensure that as many reference cells as possible reach the reference voltage value during the charging process. Furthermore, the selection of the reference voltage value is not unique, and multiple reference voltage values ​​can be selected for calculation.

[0077] It is understood that in the embodiments of this application, any battery cell can be selected as a reference cell, the voltage change curve of the reference cell can be extracted from the charging data, the reference voltage value can be determined according to the voltage change curve, and the charging time when each battery cell reaches the reference voltage value and the charging current at that charging time can be recorded during each charging process.

[0078] In step S103, the charging deviation capacity of each battery cell in each charge-discharge cycle is fitted to obtain a curve of the charging deviation capacity changing with the number of cycles. Based on the slope of the fitted curve, abnormal battery cells with self-discharge abnormalities are identified.

[0079] It is understood that, according to the calculation results of each charging cycle, the curve of the relative charging deviation capacity changing with the number of charge and discharge cycles can be obtained, the slope of the relative charging deviation capacity change can be fitted, and the self-discharge abnormality of each battery cell can be determined based on the fitted slope value of the relative charging deviation capacity. The self-discharge determination is directly made by using the slope of the relative charging deviation capacity change over a long period of time, without the need for pre-test calibration, establishment of complex models or additional complex calculations, thus reducing the amount of calculation and complexity of determining self-discharge abnormal batteries.

[0080] In this embodiment of the application, fitting the charging deviation capacity of each battery cell in each discharge cycle to obtain the curve of charging deviation capacity changing with the number of cycles includes: selecting a charging cycle number window for each charging cell; determining a target cycle number range for each charging cell based on the charging cycle number window; fitting the charging deviation capacity of each charge-discharge cycle within the target cycle number range to obtain the curve of charging deviation capacity changing with the number of cycles.

[0081] It is understood that, based on the calculation results of each charging cycle, the present application can obtain the curve of the modified charging deviation capacity changing with the number of charge-discharge cycles, select the number of cycles window, and fit the slope of the change in charging deviation capacity.

[0082] Specifically, the fitting process for the slope of the corrected charging deviation capacity change is as follows: select the charging cycle number window W; on the cycle number interval [kW, k], use a linear equation of the form y=mx+b to fit the corrected charging deviation capacity, and obtain the slope m of the corrected charging deviation capacity changing with the cycle number.

[0083] In this embodiment of the application, before fitting the charging deviation capacity of each battery cell in multiple charge-discharge cycles to obtain the curve of the charging deviation capacity changing with the number of cycles, the method further includes: obtaining a correction coefficient of the charging current relative to the reference current of the reference cell; and correcting the charging deviation capacity of each battery cell in each charge-discharge cycle according to the correction coefficient.

[0084] It is understood that, before fitting the charging deviation capacity of each battery cell in multiple charge-discharge cycles to obtain the curve of the charging deviation capacity changing with the number of cycles, the embodiments of this application correct the charging deviation capacity according to the correction coefficient, thereby improving the applicability of self-discharge anomaly identification to different charging modes such as fast charging and slow charging.

[0085] Specifically, the calculation process for correcting charging deviation capacity is as follows: Select a reference current. Based on the charging deviation capacity data of each battery relative to the reference cell during charging under different constant currents in pre-testing, the relative charging deviation capacity of each battery cell is obtained under other charging currents I. x Time relative to reference current Correction factor at time The corrected charging deviation capacity of each battery cell under the reference current in the k-th charging cycle is:

[0086] In this embodiment of the application, identifying abnormal battery cells with self-discharge abnormalities among each battery cell based on the fitting slope of the curve includes: determining whether the fitting slope is greater than a first slope threshold; if the fitting slope is greater than the first slope threshold, then determining that the battery cell is an abnormal battery cell with self-discharge abnormalities.

[0087] The first slope threshold can be set as a positive threshold K+.

[0088] It is understood that, in the embodiments of this application, when the fitting slope value of a battery cell is positive and greater than the first slope threshold, the battery cell can be determined to be a self-discharge abnormal cell.

[0089] In this embodiment of the application, identifying abnormal battery cells with self-discharge abnormalities among each battery cell based on the fitting slope of the curve includes: determining whether the fitting slope is less than a second slope threshold; if the fitting slope is less than the second slope threshold, then determining the reference cell as an abnormal battery cell with self-discharge abnormalities.

[0090] The second slope threshold can be set as a negative threshold K-.

[0091] It is understood that, in the embodiments of this application, when the fitting slope value of a battery cell is negative and less than the second slope threshold, the battery cell can be determined to be a self-discharge abnormal cell.

[0092] In this embodiment of the application, after identifying abnormal battery cells with self-discharge abnormalities in each battery cell based on the curve fitting slope, the method further includes: generating preset reminder information; sending the preset reminder information to a preset terminal to provide a self-discharge abnormality reminder based on the preset reminder information.

[0093] The preset reminder message can be set to indicate that a battery cell has an abnormal self-discharge, alerting the user to the risk of thermal runaway in the power battery. The preset terminal can be the vehicle's central control display screen.

[0094] It is understood that, in this application embodiment, after identifying a battery cell with abnormal self-discharge, the user can be alerted to take timely action to avoid thermal runaway of the power battery.

[0095] In this embodiment of the application, after identifying abnormal battery cells with self-discharge abnormalities in each battery cell based on the curve fitting slope, the method further includes: identifying the identifier of the abnormal battery cell; sending the identifier to a preset terminal to locate the abnormal battery cell in the battery pack based on the identifier.

[0096] It is understood that, in the embodiments of this application, after identifying a battery cell with abnormal self-discharge in the battery pack, the abnormal battery cell can be identified, so that the user can locate the abnormal battery cell in the battery pack according to the identification and replace it in a timely manner.

[0097] The following specific embodiment illustrates the method for identifying abnormal battery cells according to this application. Figure 2 As shown, it includes the following steps:

[0098] Step 1: Based on the long-term operating data of the actual vehicle, extract the charging process in each charge-discharge cycle.

[0099] The actual vehicle operation data refers to the voltage curves of each battery cell during the vehicle's charging and discharging processes, stored in the cloud. One charge and discharge cycle for each battery cell in the operation data is considered a single charge-discharge cycle. Long-term cycles are generally considered to have ≥N charge-discharge cycle counts; the range used here is, but not limited to, N=7. When the number of charge-discharge cycles is less than N, no self-discharge anomaly detection is performed. Within each charge-discharge cycle, only the charging process data is extracted for identifying batteries with self-discharge anomalies.

[0100] Step 2: Randomly select a reference battery cell and determine the reference voltage based on the charging voltage curve.

[0101] The reference cell can be any battery cell selected during the charging and discharging process. Due to the stepped charging strategy used in actual vehicle charging, different voltage ranges [V x V y Different charging currents I were used inside. x The reference voltage value should be within the voltage range [V]. x V y The reference voltage value is selected within a certain range to ensure that as many reference cells as possible reach the reference voltage value during the charging process. Furthermore, the selection of the reference voltage value is not unique, and multiple reference voltage values ​​can be selected for calculation.

[0102] Step 3: During each charging process, record the charging time when each battery cell reaches the reference voltage value and the charging current at that time.

[0103] For real-vehicle data using stepped current charging, record the charging time when each battery cell reaches the reference voltage value and the charging current at that time.

[0104] Step 4: Calculate the charging deviation capacity of each battery cell relative to the reference cell based on the charging time of each individual battery cell.

[0105] The calculation process for the charging deviation capacity relative to the reference cell is as follows:

[0106]

[0107] In the formula, The charging time when the reference cell reaches the reference voltage in the k-th charging cycle; The charging time when the remaining battery cells reach the reference voltage in the k-th charging cycle, i = 1, 2, ..., n, for a total of n battery cells; This is the charging current of each of the remaining battery cells when they reach the reference voltage value in the kth charging cycle. This represents the charging deviation capacity of each individual battery cell relative to the reference cell when it reaches the reference voltage value in the k-th charging cycle. A positive relative charging deviation capacity indicates that the individual battery cell reaches the reference voltage before the reference cell, and the battery has a higher capacity; a negative relative charging deviation capacity indicates that the individual battery cell reaches the reference voltage after the reference cell, and the battery has a lower capacity.

[0108] Step 5: Correct the relative charging deviation capacity based on the charging current at the moment when the reference voltage value is reached.

[0109] The calculation process for correcting charging deviation capacity is as follows: Select a reference current. Based on the charging deviation capacity data of each battery relative to the reference cell during charging under different constant currents in pre-testing, the relative charging deviation capacity of each battery cell is obtained under other charging currents I. x Time relative to reference current Correction factor at time The corrected charging deviation capacity of each battery cell under the reference current in the k-th charging cycle is:

[0110] Step 6: Based on the calculation results of each charging cycle, obtain the curve of the corrected charging deviation capacity changing with the number of charge and discharge cycles, select the cycle number window, and fit the slope of the corrected charging deviation capacity change.

[0111] The specific fitting process for the slope of the corrected charging deviation capacity change is as follows: select the charging cycle number window W; on the cycle number interval [kW, k], use a linear equation of the form y=mx+b to fit the corrected charging deviation capacity, and obtain the slope m of the corrected charging deviation capacity changing with the cycle number.

[0112] Step 7: Determine whether there is any self-discharge abnormality in each battery cell based on the slope value of the corrected charging deviation capacity fitting.

[0113] The method for determining whether self-discharge is abnormal based on the fitted slope value of the corrected charging deviation capacity is as follows: if the fitted slope value of the battery cell is positive and greater than the preset positive threshold K+, then the battery cell is determined to be a self-discharge abnormal cell; if the fitted slope value of the battery cell is negative and less than the preset negative threshold K-, then the reference cell is determined to be a self-discharge abnormal cell.

[0114] The method for identifying abnormal battery cells proposed in this application can determine abnormal battery cells with self-discharge abnormalities by linearly fitting the slope of the charging deviation capacity of a battery cell compared with a reference cell over multiple cycles, based on the charging deviation capacity of the battery cell. This avoids misjudgments caused by data jumps when using only two charging processes to determine self-discharge abnormalities, enhances the applicability of battery cell self-discharge abnormality identification, and considers the influence of different charging currents on charging capacity, enabling algorithm calculations to be performed on battery cells during fast charging and slow charging switching, thereby improving the applicability of battery self-discharge abnormality identification.

[0115] Next, with reference to the accompanying drawings, an identification device for abnormal battery cells according to an embodiment of this application is described.

[0116] Figure 3 This is a block diagram of an abnormal battery cell identification device according to an embodiment of this application.

[0117] like Figure 3 As shown, the identification device 10 for the abnormal battery cell includes: an acquisition module 100, a calculation module 200, and an identification module 300.

[0118] The acquisition module 100 is used to acquire charging data of each battery cell in the battery pack through multiple charge-discharge cycles; the calculation module 200 is used to identify the charging time and charging current when each battery cell reaches the reference voltage value in the charging data, and calculate the charging deviation capacity of each battery cell relative to the reference cell of the battery pack based on the charging time and charging current; the identification module 300 is used to fit the charging deviation capacity of each battery cell in each charge-discharge cycle to obtain the curve of the charging deviation capacity changing with the number of cycles, and identify abnormal battery cells with self-discharge abnormalities based on the slope of the fitted curve.

[0119] In this embodiment of the application, the identification module 300 is further used to: determine whether the fitting slope is greater than the first slope threshold; if the fitting slope is greater than the first slope threshold, then determine that the battery cell is an abnormal battery cell with self-discharge abnormality.

[0120] In this embodiment of the application, the identification module 300 is further used to: determine whether the fitting slope is less than the second slope threshold; if the fitting slope is less than the second slope threshold, then determine that the reference cell is an abnormal battery cell with self-discharge abnormality.

[0121] In this embodiment of the application, the identification module 300 is further configured to: select a charging cycle number window for each charging cell; determine a target cycle number range for each charging cell based on the charging cycle number window; and fit the charging deviation capacity of each charge-discharge cycle within the target cycle number range to obtain a curve of the charging deviation capacity changing with the number of cycles.

[0122] In this embodiment of the application, the apparatus 10 further includes a correction module.

[0123] The correction module is used to obtain a correction coefficient for the charging current relative to the reference current of the reference cell before fitting the charging deviation capacity of each battery cell in multiple charge-discharge cycles to obtain the curve of the charging deviation capacity changing with the number of cycles; and to correct the charging deviation capacity of each battery cell in each charge-discharge cycle according to the correction coefficient.

[0124] In this embodiment of the application, the calculation module 200 is further configured to: identify the charging time of the reference cell; determine the deviation duration based on the charging time of each cell and the charging time of the reference cell; and perform time integration on the charging current within the deviation duration to obtain the charging deviation capacity of each cell relative to the reference cell.

[0125] In this embodiment of the application, the calculation module 200 is further configured to: take any cell in the battery pack as a reference cell; extract the voltage change curve of the reference cell from the charging data, and take the target voltage value in the voltage change curve as the reference voltage value; and obtain the charging time and charging current when each cell reaches the reference voltage value within any voltage range.

[0126] In this embodiment of the application, the apparatus 10 further includes an extraction module.

[0127] The extraction module is used to acquire the charge and discharge data of the battery pack before acquiring the charging data of each individual battery cell in the battery pack through multiple charge and discharge cycles; extract the actual number of charge and discharge cycles in the charge and discharge data; if the actual number of cycles is greater than the preset number, then extract the charging data of each charge and discharge cycle in the charge and discharge data, otherwise, do not identify abnormal battery cells.

[0128] In this embodiment of the application, the device 10 further includes a reminder module.

[0129] The reminder module is used to generate a preset reminder message after identifying abnormal battery cells with self-discharge abnormalities in each battery cell based on the curve fitting slope; and to send the preset reminder message to a preset terminal to provide a self-discharge abnormality reminder based on the preset reminder message.

[0130] In this embodiment of the application, the apparatus 10 further includes a sending module.

[0131] The sending module is used to identify abnormal battery cells with self-discharge abnormalities in each battery cell after identifying them based on the curve fitting slope; and to send the identifier to a preset terminal to locate the abnormal battery cell in the battery pack based on the identifier.

[0132] It should be noted that the explanation of the aforementioned method for identifying abnormal battery cells also applies to the device for identifying abnormal battery cells in this embodiment, and will not be repeated here.

[0133] The abnormal battery cell identification device proposed in the embodiments of this application can determine the abnormal battery cell with self-discharge abnormality by linearly fitting the slope of the charging deviation capacity change of the battery cell compared with the reference cell within multiple cycles. This avoids misjudgment caused by data jumps when only two charging processes are used to determine self-discharge abnormality, enhances the applicability of battery cell self-discharge abnormality identification, and considers the influence of different charging currents on charging capacity, enabling the battery cell to perform algorithm calculations when switching between fast charging and slow charging, thereby improving the applicability of battery self-discharge abnormality identification.

[0134] Figure 4 A schematic diagram of the structure of a server provided in an embodiment of this application. The server may include:

[0135] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0136] When the processor 402 executes the program, it implements the method for identifying abnormal battery cells provided in the above embodiments.

[0137] Furthermore, the server also includes:

[0138] Communication interface 403 is used for communication between memory 401 and processor 402.

[0139] The memory 401 is used to store computer programs that can run on the processor 402.

[0140] The memory 401 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0141] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0142] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0143] The processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0144] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for identifying abnormal battery cells.

[0145] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0146] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0147] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0148] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0149] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0150] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for identifying abnormal battery cells, characterized in that, The method is applied to a server, and the method includes the following steps: Acquire charging data for each individual battery cell in the battery pack after multiple charge-discharge cycles; Identify the charging time and charging current when each battery cell reaches the reference voltage value in the charging data, and calculate the charging deviation capacity of each battery cell relative to the reference cell of the battery pack based on the charging time and the charging current; By fitting the charging deviation capacity of each battery cell in each charge-discharge cycle, a curve showing the change of charging deviation capacity with the number of cycles is obtained. Based on the slope of the fitted curve, abnormal battery cells with self-discharge abnormalities are identified among the battery cells.

2. The method for identifying abnormal battery cells according to claim 1, characterized in that, The step of identifying abnormal battery cells with self-discharge abnormalities based on the slope of the fitted curve includes: Determine whether the fitted slope is greater than a first slope threshold; If the fitting slope is greater than the first slope threshold, then the battery cell is determined to be an abnormal battery cell with self-discharge abnormality.

3. The method for identifying abnormal battery cells according to claim 1 or 2, characterized in that, The step of identifying abnormal battery cells with self-discharge abnormalities based on the slope of the fitted curve includes: Determine whether the fitted slope is less than the second slope threshold; If the fitting slope is less than the second slope threshold, then the reference cell is determined to be an abnormal battery cell with self-discharge abnormality.

4. The method for identifying abnormal battery cells according to claim 1, characterized in that, The process of fitting the charging deviation capacity of each battery cell in each charge-discharge cycle to obtain a curve of charging deviation capacity changing with the number of cycles includes: Select the charging cycle count window for each individual charging cell; The target cycle number range for each charging unit is determined based on the charging cycle number window. By fitting the charging deviation capacity of each charge-discharge cycle within the target cycle range, a curve of the charging deviation capacity changing with the number of cycles is obtained.

5. The method for identifying abnormal battery cells according to claim 1 or 4, characterized in that, Before fitting the charging deviation capacity of each battery cell in multiple charge-discharge cycles to obtain the curve of charging deviation capacity changing with the number of cycles, the method further includes: Obtain the correction factor of the charging current relative to the reference current of the reference cell; The charging deviation capacity of each battery cell in each charge-discharge cycle is corrected according to the correction factor.

6. The method for identifying abnormal battery cells according to claim 1, characterized in that, The calculation of the charging deviation capacity of each battery cell relative to a reference cell of the battery pack based on the charging time and the charging current includes: Identify the charging time of the reference cell; The deviation duration is determined based on the charging time of each battery cell and the charging time of the reference cell; By integrating the charging current over the deviation period, the charging deviation capacity of each battery cell relative to the reference cell is obtained.

7. The method for identifying abnormal battery cells according to claim 1, characterized in that, The step of identifying the charging time and charging current when each battery cell in the charging data reaches the reference voltage value includes: Take any one of the battery cells in the battery pack as a reference cell; Extract the voltage change curve of the reference cell from the charging data, and use the target voltage value in the voltage change curve as the reference voltage value; Obtain the charging time and charging current of each battery cell when it reaches the reference voltage value within any voltage range.

8. The method for identifying abnormal battery cells according to claim 1, characterized in that, Before acquiring charging data from multiple charge-discharge cycles of each individual cell in the battery pack, the following steps are also included: Obtain battery pack charge and discharge data; Extract the actual number of charge-discharge cycles from the charge-discharge data; If the actual number of times exceeds the preset number of times, the charging data for each charge-discharge cycle is extracted from the charge-discharge data; otherwise, abnormal battery cells are not identified.

9. The method for identifying abnormal battery cells according to claim 1, characterized in that, After identifying abnormal battery cells with self-discharge anomalies based on the slope of the curve, the method further includes: Generate preset reminder messages; The preset reminder information is sent to a preset terminal to provide an alert for abnormal self-discharge based on the preset reminder information.

10. The method for identifying abnormal battery cells according to claim 1, characterized in that, After identifying abnormal battery cells with self-discharge anomalies based on the slope of the curve, the method further includes: Identify the identifier of the abnormal battery cell; The identifier is sent to a preset terminal to locate the abnormal battery cell in the battery pack based on the identifier.

11. A device for identifying abnormal battery cells, characterized in that, The device is used in a server, wherein the device includes: The acquisition module is used to acquire charging data of each battery cell in the battery pack during multiple charge-discharge cycles. The calculation module is used to identify the charging time and charging current when each battery cell in the charging data reaches the reference voltage value, and to calculate the charging deviation capacity of each battery cell relative to the reference cell of the battery pack based on the charging time and the charging current. The identification module is used to fit the charging deviation capacity of each battery cell in each charge-discharge cycle to obtain a curve of the charging deviation capacity changing with the number of cycles, and to identify abnormal battery cells with self-discharge abnormalities based on the slope of the fitted curve.

12. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for identifying abnormal battery cells as described in any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for identifying abnormal battery cells as described in any one of claims 1-10.

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