A method, device and equipment for detecting and evaluating gradual failure of lithium battery and storage medium

Abstract

The application discloses a kind of lithium battery gradual fault detection and evaluation method, device, equipment and storage medium, the method includes: according to the end voltage and median end voltage of each battery monomer in lithium ion battery pack, corresponding difference voltage curve and median difference voltage curve are generated;For each battery monomer, the Mahalanobis distance between corresponding difference voltage curve and median difference voltage curve is calculated, when Mahalanobis distance is greater than the preset threshold value, determine that the battery monomer occurs micro short circuit fault;When the first discharge and charge cycle of the first discharge and charge cycle and the second discharge and charge cycle are carried out on the battery monomer twice adjacent, the first charge voltage curve and the second charge voltage curve of the second discharge and charge cycle are obtained, the micro short circuit current and micro short circuit resistance of the battery monomer are calculated, and the fault degree of the lithium ion battery pack is evaluated.The detection accuracy of lithium battery gradual fault and the evaluation efficiency of fault degree can be improved by the application.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion battery fault diagnosis technology, and in particular to a method, apparatus, device, and storage medium for detecting and evaluating gradual faults in lithium batteries. Background Technology

[0002] Lithium-ion batteries, with their advantages of high power density, low self-discharge rate, long cycle life, and no memory effect, have been widely used in electric vehicles and grid energy storage. However, lithium-ion batteries have potential safety issues, especially accidents characterized by thermal runaway, which have raised public concerns about their safety. In recent years, analysis reports of battery pack spontaneous combustion accidents from various research institutions have indicated that internal short circuits are one of the main causes of thermal runaway in lithium-ion batteries. Internal short circuits are gradual, meaning they are a progressive fault. Specifically, an internal short circuit typically undergoes a long evolution and development process before deteriorating into thermal runaway. In early internal short circuits (i.e., micro-short circuits), the equivalent short-circuit resistance is relatively large, and the changes in battery electrothermal parameters caused by the short circuit are not obvious, making them highly concealed. This also makes early micro-short circuit faults difficult to detect. Therefore, timely diagnosis of early micro-short circuit faults is a crucial and challenging task for ensuring the safe and stable operation of lithium-ion batteries.

[0003] Existing diagnostic methods for micro-short circuits in battery packs can be divided into two categories based on their diagnostic effectiveness: qualitative detection and quantitative diagnosis. Qualitative detection methods cannot quantitatively calculate short-circuit current and short-circuit resistance, i.e., they cannot provide a quantitative description of the severity of the short circuit. Existing quantitative diagnostic methods have the following limitations: (1) Existing methods require the establishment of an equivalent circuit model for each individual cell in the battery pack. The equivalent circuit model of a lithium-ion battery is difficult to reflect the internal reaction mechanism of the battery, and its accuracy is greatly affected by the model parameters, so the accuracy of the diagnostic results is limited by the accuracy of the model itself and is easily affected by the model parameters. (2) Existing methods require the calculation of the short-circuit current and short-circuit resistance of each individual cell in the battery pack, and the determination of whether a fault has occurred is based on the short-circuit current and short-circuit resistance. This method of calculating the short-circuit resistance indiscriminately lacks specificity, and for battery packs composed of hundreds or thousands of individual cells, it will increase the computational burden of the battery management system (BMS). Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for detecting and evaluating gradual faults in lithium batteries, in order to solve the technical problem that existing methods require establishing an equivalent circuit model for each individual cell in the battery pack and calculating the short-circuit current and short-circuit resistance of each individual cell in the battery pack, and then determining whether a fault has occurred based on the short-circuit current and short-circuit resistance.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a method for detecting and evaluating gradual faults in lithium batteries, comprising: Obtain the terminal voltage of each cell in the lithium-ion battery pack, sort the terminal voltage of each cell, and take the median of the sorted terminal voltages as the median terminal voltage of the corresponding cell. Based on the terminal voltage, calculate the differential voltage corresponding to each battery cell and generate the corresponding differential voltage curve. Based on the median terminal voltage, calculate the median differential voltage corresponding to the lithium-ion battery pack and generate the corresponding median differential voltage curve. For each battery cell, the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated, and the Mahalanobis distance is compared with a preset threshold. When the Mahalanobis distance is greater than the preset threshold, it is determined that the battery cell has a micro short circuit fault. For a battery cell that has experienced a micro short circuit fault, the first charging voltage curve of the first discharge and charging cycle and the second charging voltage curve of the second discharge and charging cycle are obtained when the battery cell is subjected to two adjacent discharge and charging cycles. Based on the first charging voltage curve and the second charging voltage curve, the micro-short-circuit current and micro-short-circuit resistance of the battery cell are calculated, and then the degree of failure of the lithium-ion battery pack is evaluated based on the micro-short-circuit current and micro-short-circuit resistance.

[0006] As a preferred embodiment, before calculating the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, the method further includes: The differential voltage curve and the median differential voltage curve are smoothed according to a preset moving average filter. The moving average filter is: ; in, s r Original signal s In the r The value at time, For the corresponding filtered value, 2 N p +1 represents the window size of the moving average filter. N p It is an integer. l This represents the time lag number. s r-l For the original signal at the 1st r - l The value at time.

[0007] As a preferred approach, the differential voltage corresponding to each individual battery cell or the median differential voltage corresponding to the lithium-ion battery pack is calculated using the following formula: ; Where DV is the differential voltage corresponding to each individual battery cell or the median differential voltage corresponding to the lithium-ion battery pack. Q To charge the battery capacity, V This refers to the terminal voltage in constant current charging mode. Q 2 -Q 1 represents the voltage interval Δ V Changes in internal charging capacity.

[0008] As a preferred embodiment, the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated using the following formula: ; ; ; in, The Mahalanobis distance between the differential voltage curve and the median differential voltage curve. D The differential voltage curves are for individual battery cells. D med This is the median differential voltage curve for a lithium-ion battery pack. N The lengths of the differential voltage curve and the median differential voltage curve are given. μ Σ is the mean vector of the differential voltage curve and the median differential voltage curve; Σ is the covariance matrix between the differential voltage curve and the median differential voltage curve.

[0009] As a preferred embodiment, the step of calculating the micro-short-circuit current and micro-short-circuit resistance of the battery cell based on the first charging voltage curve and the second charging voltage curve includes: Based on the second charging voltage curve, the voltage at the end of the second discharge and charging cycle is obtained, and the voltage is interpolated on the first charging voltage curve. The time obtained after interpolation is taken as the first time. The moment when the first discharge and the end of the charging cycle are taken as the second moment, and the time interval between the first moment and the second moment is taken as the remaining charging time. The charging current data during the first discharge and charge cycle and the second discharge and charge cycle are obtained. Based on the remaining charging time and the charging current data, the corresponding remaining charging capacity is calculated. Then, based on the remaining charging capacity, the micro-short-circuit current and micro-short-circuit resistance of the battery cell are calculated.

[0010] As a preferred method, the remaining charging capacity is calculated using the following formula: ; in, Q Δ For the remaining charging capacity, Δ t For the remaining charging time, This contains charging current data for the first and second discharge and charge cycles.

[0011] As a preferred embodiment, the micro-short-circuit current of the battery cell is calculated using the following formula: ; in, I err This refers to the micro-short-circuit current of a single battery cell. T c The time of one discharge and charge cycle; The micro short-circuit resistance of the battery cell can be calculated using the following formula: ; in, The micro short-circuit resistance of a single battery cell. U This is the average voltage over the discharge and charge cycles.

[0012] Based on the above embodiments, another embodiment of the present invention provides a lithium battery gradual fault detection and evaluation device, including: a terminal voltage acquisition module, a differential voltage calculation module, a micro short circuit fault judgment module, a charging voltage curve acquisition module, and a fault degree evaluation module. The terminal voltage acquisition module is used to acquire the terminal voltage of each battery cell in the lithium-ion battery pack, sort the terminal voltage of each battery cell, and take the median of the sorted terminal voltages as the median terminal voltage of the corresponding battery cell. The differential voltage calculation module is used to calculate the differential voltage corresponding to each battery cell based on the terminal voltage and generate the corresponding differential voltage curve; and to calculate the median differential voltage corresponding to the lithium-ion battery pack based on the median terminal voltage and generate the corresponding median differential voltage curve. The micro-short circuit fault detection module is used to calculate the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve for each battery cell, and compare the Mahalanobis distance with a preset threshold. When the Mahalanobis distance is greater than the preset threshold, it is determined that the battery cell has a micro-short circuit fault. The charging voltage curve acquisition module is used to acquire, for a battery cell that has experienced a micro short circuit fault, the first charging voltage curve of the first discharge and charging cycle and the second charging voltage curve of the second discharge and charging cycle when the battery cell is subjected to two adjacent discharge and charging cycles. The fault severity assessment module is used to calculate the micro-short-circuit current and micro-short-circuit resistance of the battery cell based on the first charging voltage curve and the second charging voltage curve, and then assess the fault severity of the lithium-ion battery pack based on the micro-short-circuit current and micro-short-circuit resistance.

[0013] Based on the above embodiments, another embodiment of the present invention provides an electronic device, the device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the lithium battery gradual fault detection and evaluation method described in the above embodiments of the invention.

[0014] Based on the above embodiments, another embodiment of the present invention provides a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the lithium battery gradual fault detection and evaluation method described in the above embodiments of the invention.

[0015] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention provides a method for detecting and evaluating gradual faults in lithium batteries. First, it detects whether each individual cell in the lithium battery pack has a micro-short circuit fault: The terminal voltage of each individual cell in the lithium-ion battery pack is acquired and sorted. The median of the sorted terminal voltages is taken as the median terminal voltage of the corresponding cell. Based on the terminal voltage, the differential voltage corresponding to each cell is calculated, and a corresponding differential voltage curve is generated. Based on the median terminal voltage, the median differential voltage of the lithium-ion battery pack is calculated, and a corresponding median differential voltage curve is generated. For each cell, the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated, and the Mahalanobis distance is compared with a preset threshold. When the Mahalanobis distance is greater than the preset threshold, the cell is determined to have a micro-short circuit fault.

[0016] Then, the degree of micro-short-circuit fault in the battery cell is assessed: For the battery cell that has a micro-short-circuit fault, the first charging voltage curve of the first discharge and charge cycle and the second charging voltage curve of the second discharge and charge cycle are obtained when the battery cell is subjected to two adjacent discharge and charge cycles; based on the first charging voltage curve and the second charging voltage curve, the micro-short-circuit current and micro-short-circuit resistance of the battery cell are calculated, and then the degree of fault of the lithium-ion battery pack is assessed based on the micro-short-circuit current and micro-short-circuit resistance.

[0017] Therefore, the method of this invention does not require the establishment of a battery model in detecting whether micro-short-circuit faults have occurred in individual battery cells within a lithium battery pack and in assessing the degree of these faults. This avoids the difficulty of establishing accurate battery models and improves the detection accuracy of gradual faults in lithium batteries. Furthermore, the method proposed in this invention adopts a detection-then-quantitative evaluation approach, calculating the micro-short-circuit current and resistance only for battery cells confirmed to have experienced micro-short-circuit faults. This makes the calculation process highly targeted, avoiding a large amount of unnecessary calculations and improving evaluation efficiency. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for detecting and evaluating gradual faults in lithium batteries according to an embodiment of the present invention. Figure 2 This is a diagram showing the remaining charging capacity and remaining charging time; Figure 3 This is a schematic diagram for judging micro-short circuit faults based on changes in remaining charging capacity; Figure 4 This is a schematic diagram showing the remaining charging time of the battery due to micro-short circuit leakage at the end of two cycles; Figure 5 This is a schematic diagram of the Mahalanobis distances of each cell when no micro-short circuit occurs in cycle 1; Figure 6 This is a schematic diagram of the Mahalanobis distances of each cell when no micro-short circuit occurs in cycle 2; Figure 7 This is a schematic diagram of the Mahalanobis distance of each cell when the short-circuit resistance of Cycle 3 is 300Ω; Figure 8 This is a schematic diagram of the Mahalanobis distance of each cell when the short-circuit resistance of cycle 5 is 200Ω; Figure 9 This is a schematic diagram of the Mahalanobis distance of each cell when the short-circuit resistance of cycle 7 is 100Ω; Figure 10 This is a schematic diagram of the Mahalanobis distance of each cell when the short-circuit resistance of Cycle 9 is 50Ω; Figure 11 This is a schematic diagram of the Mahalanobis distance of each cell when the short-circuit resistance of cycle 11 is 10Ω; Figure 12 This is a schematic diagram of the Mahalanobis distance of each cell when the short-circuit resistance of cycle 13 is 5Ω; Figure 13 This is a schematic diagram of the structure of a lithium battery gradual fault detection and evaluation device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0021] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0024] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0025] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0026] Example 1 Please refer to Figure 1 The above is a flowchart illustrating a method for detecting and evaluating gradual faults in lithium batteries according to an embodiment of the present invention, including the following specific steps: S1. Obtain the terminal voltage of each battery cell in the lithium-ion battery pack, sort the terminal voltage of each battery cell, and take the median of the sorted terminal voltages as the median terminal voltage of the corresponding battery cell. Specifically, this invention proposes a method for early internal short-circuit fault detection and quantitative assessment of energy storage lithium-ion battery packs based on DV analysis and Mahalanobis distance. The implementation process of the proposed method mainly includes three steps: (1) Considering that the median is not affected by the maximum and minimum values, the terminal voltages between individual cells in the lithium-ion battery pack are sorted at any time to obtain the median terminal voltage curve. Based on this, the median differential voltage curve is extracted and used as a reference standard to represent the state of normal cells in the battery pack; (2) The Mahalanobis distance between the differential voltage curve and the median differential voltage curve corresponding to each cell is calculated. If the Mahalanobis distance of a cell is greater than the threshold, it indicates that it has deviated from the normal state and is judged as a micro-short-circuited cell; conversely, cells with a Mahalanobis distance less than or equal to the threshold are identified as normal cells; (3) For the micro-short-circuited cells detected in step 2, the short-circuit current and short-circuit resistance are calculated based on the difference in charging voltage curves between adjacent cycles to quantitatively assess the severity of the short-circuit fault.

[0027] As can be seen, the method of this invention does not require the establishment of a battery model in the process of detecting micro-short-circuited cells within the battery pack and calculating their short-circuit resistance, thus avoiding the difficulty of establishing an accurate battery model. Furthermore, the method proposed in this invention adopts a technical approach of detection followed by quantitative evaluation, which makes the short-circuit resistance estimation process highly targeted, avoiding a large amount of unnecessary calculations and improving diagnostic efficiency. The extraction of differential voltage curves, micro-short-circuit detection based on Mahalanobis distance, and short-circuit resistance estimation based on changes in charging voltage curves are described in detail below: 1. Extraction of differential voltage curves (1) Obtain the terminal voltage of each cell in the lithium-ion battery pack, sort the terminal voltage of each cell, and take the median of the sorted terminal voltages as the median terminal voltage of the corresponding cell.

[0028] S2. Based on the terminal voltage, calculate the differential voltage corresponding to each battery cell and generate the corresponding differential voltage curve. Based on the median terminal voltage, calculate the median differential voltage corresponding to the lithium-ion battery pack and generate the corresponding median differential voltage curve. Preferably, the differential voltage corresponding to each individual battery cell or the median differential voltage corresponding to the lithium-ion battery pack is calculated using the following formula: ; Where DV is the differential voltage corresponding to each individual battery cell or the median differential voltage corresponding to the lithium-ion battery pack. Q To charge the battery capacity, V This refers to the terminal voltage in constant current charging mode. Q 2 -Q 1 represents the voltage interval Δ V Changes in internal charging capacity.

[0029] Preferably, before calculating the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, the method further includes: smoothing the differential voltage curve and the median differential voltage curve according to a preset moving average filter; The moving average filter is: ; in, s r Original signal s In the r The value at time, For the corresponding filtered value, 2 N p +1 represents the window size of the moving average filter. N p It is an integer. l This represents the time lag number. s r-l For the original signal at the 1st r - l The value at time.

[0030] (2) As mentioned above, after obtaining the median terminal voltage between cells in the lithium-ion battery pack, it is necessary to extract the median differential voltage. Furthermore, it is necessary to calculate the differential voltage of each cell based on its terminal voltage. This invention uses a numerical differentiation method to obtain the differential voltage, and the calculation formula is as follows: (1) in,Q Indicates battery charging capacity. V It is the terminal voltage in constant current charging mode. Equation (1) represents the voltage based on equal voltage interval Δ. V Calculate DV, Q 2 -Q 1 indicates that in the voltage interval Δ V The charging capacity change within the curve. This invention calculates DV based on the EVI method. To fully capture the characteristics of the DV curve, Δ... V Set to 1 mV. The median DV curve and the individual cell DV curve are both obtained by formula (1), that is, the calculation process is the same. The only difference is that when calculating the median DV curve, the median terminal voltage is input; when calculating the individual cell DV curve, the terminal voltage of the corresponding individual cell is input.

[0031] The DV curve calculated using the numerical differentiation method described above is susceptible to measurement noise. Therefore, it is necessary to use a suitable filter to obtain a smooth DV curve. This invention employs a moving average filter to smooth the DV curve. The moving average filter smooths the signal by calculating the average value of the signal within a preset window. Given a time-varying signal s contaminated by noise, the moving average filter can be designed as follows: (2) in, s r Represents the original signal s In the r The value at time, That is the corresponding filtered value, 2 N p +1 is the window size of the moving average filter and N p It is an integer. l This represents the time lag number. s r-l For the original signal at the 1st r - l The value at time.

[0032] S3. For each battery cell, calculate the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, and compare the Mahalanobis distance with a preset threshold. When the Mahalanobis distance is greater than the preset threshold, it is determined that the battery cell has a micro short circuit fault. Preferably, the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated using the following formula: ; ; ; in, The Mahalanobis distance between the differential voltage curve and the median differential voltage curve. D The differential voltage curves are for individual battery cells. D med This is the median differential voltage curve for a lithium-ion battery pack. N Σ is the length of the differential voltage curve and the median differential voltage curve, μ is the mean vector of the differential voltage curve and the median differential voltage curve, and Σ is the covariance matrix between the differential voltage curve and the median differential voltage curve.

[0033] 2. Micro-short-circuit cell detection based on Mahalanobis distance After extracting the median differential voltage curve and the differential voltage curves of each individual cell, this invention uses Mahalanobis distance to measure the similarity between the differential voltage curves of each individual cell and the median differential voltage curve, thereby detecting micro-short-circuited cells within the battery pack. Mahalanobis distance considers not only the Euclidean distance between two points but also their covariance structure, thus exhibiting higher sensitivity under different distributions and scales. Specifically, this invention represents the median differential voltage curve and the differential voltage curve of a specific individual cell as follows: D med and D : (3) (4) in N This represents the lengths of the median differential voltage curve and the individual differential voltage curve. In multivariate applications, Mahalanobis distance is of greater interest than Euclidean distance because the former is dimensionless and takes into account the correlation of variables. Assuming the median differential voltage curve... D med Differential voltage curve of individual cells D The mean vector is μ If the covariance matrix between them is Σ, then the sample vector D med Compared to μ The multivariate Mahalanobis distance is defined as: (5) D med and D The Mahalanobis distance between them is defined as: (6) Based on the above process, the Mahalanobis distance for each cell in the battery pack can be calculated. Cells with a Mahalanobis distance greater than a threshold are detected as having micro-short circuits, while cells with a Mahalanobis distance less than the threshold are identified as normal cells.

[0034] S4. For a battery cell that has a micro short circuit fault, obtain the first charging voltage curve of the first discharge and charging cycle and the second charging voltage curve of the second discharge and charging cycle when the battery cell is subjected to two adjacent discharge and charging cycles. 3. Fault severity assessment based on changes in charging voltage curve After detecting micro-short-circuited cells within the battery pack, this invention further estimates their short-circuit current and short-circuit resistance to quantitatively assess the severity and evolution stage of the micro-short circuit. The principle behind the estimation of short-circuit current and short-circuit resistance is explained below: For the same type of lithium battery, its SOC-OCV curve is fixed and unchanging. Existing research suggests that the difference between the charging voltage curve and the open circuit voltage curve of lithium-ion batteries is caused by electrode polarization. The hypothesis of charging voltage curve consistency has been proposed and verified, that is, for the same type of single cell with different internal resistance, different SOC, and different capacity, their charging voltage curves can be superimposed by translation or stretching transformation.

[0035] Please refer to Figure 2 This diagram illustrates the remaining charge capacity and remaining charging time. In a series-connected battery pack, when two individual cells have different initial SOCs, the charge capacity of both cells remains the same during continuous charging of the entire pack. Figure 2 As shown, when monomer 2 is in t Once the battery reaches its threshold voltage at time 0 and is fully charged, the battery pack stops charging to prevent overcharging. At this point, cell 1 is approximately Δ away from being fully charged. Q That is, the remaining charging capacity. Shifting the terminal voltage curve of cell 1 to the curve of cell 2, we obtain... t 0-Δ t This point marks the end of the curve after the translation of cell 1. Based on the consistency of the charging voltage curve, if cell 1 continues to be charged until... t 0+Δ t Then both monomers are in a fully charged state. Time difference Δ t This is the remaining charging time. Given the charging current data, the remaining charging capacity can be calculated using the remaining charging time.

[0036] Please refer to Figure 3 This diagram illustrates how changes in remaining charge level can be used to diagnose micro-short-circuit faults. Based on this, existing research has proposed a micro-short-circuit fault diagnosis method. For batteries experiencing micro-short-circuit faults, additional charge will be lost due to short-circuit leakage, such as... Figure 3As shown, a micro-short circuit fault is determined by comparing the change in remaining charge capacity of a partially charged battery at the end of two discharge-charge cycles. The change in remaining charge capacity is calculated using the difference in remaining charging time, which is then used to calculate the short-circuit current and short-circuit resistance. However, this method requires at least one cell in the battery pack to be fully charged each time, which can lead to long detection cycles under many dynamic operating conditions, making it difficult to detect micro-short circuit problems in a timely manner. Furthermore, this method uses the voltage curve of a normal cell as a reference to determine the remaining charging time of the micro-short-circuited cell. In other words, it calculates the remaining charging time of the micro-short-circuited cell by comparing its voltage curve with that of a normal cell. This horizontal comparison is susceptible to interference from inconsistencies between cells within the battery pack, introducing significant errors into the short-circuit resistance calculation.

[0037] Please refer to Figure 4 This diagram illustrates the remaining charging time of the battery due to micro-short-circuit leakage at the end of two cycles. The method employed in this invention estimates the short-circuit current and short-circuit resistance by analyzing the difference in charging voltage curves between adjacent cycles. Specifically, the latter charge-discharge cycle is designated as cycle 2, and the former as cycle 1. The first charging voltage curve of cycle 1 and the second charging voltage curve at the end of cycle 2 are obtained and compared. Figure 4 As shown.

[0038] S5. Calculate the micro-short-circuit current and micro-short-circuit resistance of the battery cell based on the first charging voltage curve and the second charging voltage curve, and then assess the fault level of the lithium-ion battery pack based on the micro-short-circuit current and micro-short-circuit resistance.

[0039] Preferably, the step of calculating the micro-short-circuit current and micro-short-circuit resistance of the battery cell based on the first charging voltage curve and the second charging voltage curve includes: obtaining the voltage at the end of the second discharge and charge cycle based on the second charging voltage curve, interpolating the voltage on the first charging voltage curve, and taking the interpolated time as the first moment; taking the end of the first discharge and charge cycle as the second moment, and taking the time interval between the first moment and the second moment as the remaining charging time; obtaining the charging current data during the first discharge and charge cycle and the second discharge and charge cycle, calculating the corresponding remaining charging capacity based on the remaining charging time and the charging current data, and then calculating the micro-short-circuit current and micro-short-circuit resistance of the battery cell based on the remaining charging capacity.

[0040] Preferably, the remaining charging capacity is calculated using the following formula: ; in, Q Δ For the remaining charging capacity, Δ t For the remaining charging time, This contains charging current data for the first and second discharge and charge cycles.

[0041] Preferably, the micro short-circuit current of the battery cell is calculated using the following formula: ; in, I err This refers to the micro-short-circuit current of a single battery cell. T c The time of one discharge and charge cycle; The micro short-circuit resistance of the battery cell can be calculated using the following formula: ; in, U is the micro short-circuit resistance of a single battery cell, and U is the average voltage during discharge and charge cycles.

[0042] Then, the voltage at the end of cycle 2 is interpolated onto the voltage curve of cycle 1, and the corresponding time after interpolation is recorded as . t 1. Record the time when loop 1 ends as t 2, then t 1 and t The time interval between 2 is the remaining charging time Δ. t That is, at the end of cycle 2, an additional Δ is required. t It will take time to reach the same state as when loop 1 ended.

[0043] The remaining charging capacity can be calculated using the calculated remaining charging time and charging current data. Q Δ： (7) Micro short circuit current I err From equation (8), we obtain that, T c The time for one cycle is: (8) After calculating the short-circuit current, the average voltage during charging and discharging is used. U Calculate the short-circuit resistance: (9) After calculating the short-circuit resistance and short-circuit current, they can be directly used as scientific indicators to judge the severity of short-circuit faults. The larger the short-circuit current and the smaller the short-circuit resistance, the more severe the short-circuit fault, and the higher the possibility of thermal runaway in the lithium battery.

[0044] The method proposed in this invention differs from the method mentioned in the previous section in two ways: (1) Instead of using full charge as the end of a charge-discharge cycle, the period during which the SOC change calculated by the ampere-hour integration method is 0 is used as a micro-short circuit fault judgment period. During this period, normal batteries do not have leakage, so the ampere-hour integration method is accurate, and the voltage is equal at the end of the cycle compared to the beginning. However, batteries with micro-short circuit faults will show a decrease in voltage at the end of the cycle compared to the beginning due to short circuit leakage. This allows for multiple detections under multiple discharge-charge conditions, shortening the judgment period, enabling diagnosis even under conditions where full charge is not achieved for extended periods, and allowing for more frequent monitoring of micro-short circuit changes.

[0045] (2) Instead of calculating the remaining charge capacity of a single battery cell before full charge, this method uses the cell's own voltage at the start of each discharge-charge cycle as a benchmark. At the end of the cycle, it calculates the remaining charging time to this benchmark voltage, thus obtaining the remaining charge capacity. This remaining capacity represents the leakage current caused by a short circuit fault within the battery. The short-circuit current and short-circuit resistance are then calculated. This method does not rely on battery consistency and avoids the need to shift or scale the charging voltage curve due to inconsistencies, simplifying the algorithm. More importantly, this approach avoids interference from inherent inconsistencies between individual cells within the battery pack, improving the accuracy of short-circuit resistance estimation.

[0046] In one specific embodiment, the present invention was tested on a battery pack consisting of eight cylindrical lithium-ion battery cells connected in series to verify the effectiveness of the above method. The specifications of the individual cells are shown in Table 1. Table 1 Lithium-ion battery specifications The battery pack was charged at a constant current of 0.5 C. Charging was stopped when the maximum single-cell terminal voltage reached the charging cutoff voltage of 4.2 V to prevent overcharging of individual cells. Next, a dynamic stress discharge test was performed. Discharging was stopped when the minimum single-cell terminal voltage reached the discharge cutoff voltage of 2.75 V to prevent over-discharging of individual cells. A total of 14 charge-discharge cycles were performed on the battery pack.

[0047] This invention simulates micro-short-circuit faults by connecting an external resistor in parallel across the battery terminals. This method can effectively control the triggering time and location of micro-short-circuit faults and simulate their evolution process, exhibiting good controllability and repeatability.

[0048] Specifically, to simulate the evolution of a micro-short circuit, short-circuit resistors of different amplitudes and corresponding manual switches were connected in series and then in parallel to cells 4 and 8. By closing different switches, short-circuit resistors of different amplitudes could be connected in parallel to the battery terminals to simulate the occurrence and evolution of a micro-short circuit. The smaller the short-circuit resistance, the larger the short-circuit current, indicating a more severe short circuit. The parallel short-circuit resistors corresponding to each charge-discharge cycle are shown in Table 2: Table 2 Short-circuit resistance for different charge-discharge cycles Table 2 Short-circuit resistance for different charge-discharge cycles Next, the Mahalanobis distance between the DV curve and the median DV curve of each cell is calculated and compared with a predetermined threshold to detect micro-short-circuited cells. Specifically, for the battery pack used in this invention, the fault detection threshold is set to 0.10. It should be noted that in actual use, the fault detection threshold can be flexibly adjusted according to battery type, application scenario, etc., to improve the sensitivity of the fault detection algorithm.

[0049] Please refer to Figures 5-12 The diagrams show the Mahle distances of individual cells in the battery pack under different charge-discharge cycles: Cycle 1 (no micro-short circuit), Cycle 2 (no micro-short circuit), Cycle 3 (short circuit resistance 300Ω), Cycle 5 (short circuit resistance 200Ω), Cycle 7 (short circuit resistance 100Ω), Cycle 9 (short circuit resistance 50Ω), Cycle 11 (short circuit resistance 10Ω), and Cycle 13 (short circuit resistance 5Ω). Figures 5 to 12 As shown, the dashed line represents the threshold. From Figures 5 to 12It can be observed that in the first two cycles, the Mahalanobis distance of all cells is below the threshold, indicating that all cells are in a normal state and no micro-short circuits have occurred during the first two cycles. Starting from the third cycle, the Mahalanobis distances of cells 4 and 8 exceed the threshold, while the Mahalanobis distances of other cells remain below the threshold. This indicates that micro-short circuits have occurred in cells 4 and 8 starting from the third cycle, while other cells remain in a normal state. Furthermore, it can be observed that the Mahalanobis distances of cells 4 and 8 increase as the short-circuit resistance decreases, indicating that the Mahalanobis distance of micro-short-circuited cells gradually increases as the micro-short circuit worsens. These diagnostic results are consistent with the aforementioned fault injection situation, demonstrating that the proposed method can accurately detect micro-short-circuited cells within lithium-ion battery packs, proving the effectiveness of the proposed method. Observing the fault detection results from the 4th, 6th, 8th, 10th, 12th, and 14th charge-discharge cycles yields the same conclusion, which will not be elaborated upon further in this paper for simplicity.

[0050] After detecting micro-short-circuited cells in the battery pack, their short-circuit resistances are further estimated using the previously proposed method to quantify the severity and evolution stage of the micro-short circuit. The actual, estimated, and relative errors of the short-circuit resistances for micro-short-circuited cells 4 and 8 are listed in Table 3. As shown in Table 3, the maximum relative error of the short-circuit resistance estimation results is 5.21% for micro-short-circuited cell 4 and 4.89% for micro-short-circuited cell 8. Furthermore, Table 3 also shows that as the actual short-circuit resistance connected in parallel across the battery decreases, the relative error of the estimation results generally decreases. This is because as the short-circuit resistance decreases, the short-circuit current gradually increases, and the fault characteristics caused by the micro-short circuit become increasingly apparent. The above-mentioned estimation errors of the short-circuit resistance are within an acceptable range, demonstrating the effectiveness of the proposed quantitative assessment method for micro-short circuits. Table 3. Estimation results of micro-short-circuit resistance Example 2 Please refer to Figure 13 This is a schematic diagram of a lithium battery gradual fault detection and evaluation device provided in an embodiment of the present invention. The device includes: a terminal voltage acquisition module, a differential voltage calculation module, a micro short circuit fault judgment module, a charging voltage curve acquisition module, and a fault degree evaluation module. The terminal voltage acquisition module is used to acquire the terminal voltage of each battery cell in the lithium-ion battery pack, sort the terminal voltage of each battery cell, and take the median of the sorted terminal voltages as the median terminal voltage of the corresponding battery cell. The differential voltage calculation module is used to calculate the differential voltage corresponding to each battery cell based on the terminal voltage and generate the corresponding differential voltage curve; and to calculate the median differential voltage corresponding to the lithium-ion battery pack based on the median terminal voltage and generate the corresponding median differential voltage curve. The micro-short circuit fault detection module is used to calculate the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve for each battery cell, and compare the Mahalanobis distance with a preset threshold. When the Mahalanobis distance is greater than the preset threshold, it is determined that the battery cell has a micro-short circuit fault. The charging voltage curve acquisition module is used to acquire, for a battery cell that has experienced a micro short circuit fault, the first charging voltage curve of the first discharge and charging cycle and the second charging voltage curve of the second discharge and charging cycle when the battery cell is subjected to two adjacent discharge and charging cycles. The fault severity assessment module is used to calculate the micro-short-circuit current and micro-short-circuit resistance of the battery cell based on the first charging voltage curve and the second charging voltage curve, and then assess the fault severity of the lithium-ion battery pack based on the micro-short-circuit current and micro-short-circuit resistance.

[0051] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0052] Those skilled in the art will clearly understand that, for convenience and simplicity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0053] Example 3 Accordingly, embodiments of the present invention provide an electronic device, the device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the lithium battery gradual fault detection and evaluation method described in the above embodiments of the invention.

[0054] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0055] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

[0056] Example 4 Accordingly, embodiments of the present invention provide a storage medium, the storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to execute the lithium battery gradual fault detection and evaluation method described in the above embodiments of the invention.

[0057] The memory can be used to store the computer program. The processor implements various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0058] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0059] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for detecting and evaluating gradual faults in lithium batteries, characterized in that, include: Obtain the terminal voltage of each cell in the lithium-ion battery pack, sort the terminal voltage of each cell, and take the median of the sorted terminal voltages as the median terminal voltage of the corresponding cell. Based on the terminal voltage, calculate the differential voltage corresponding to each battery cell and generate the corresponding differential voltage curve. Based on the median terminal voltage, calculate the median differential voltage corresponding to the lithium-ion battery pack and generate the corresponding median differential voltage curve. For each battery cell, the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated, and the Mahalanobis distance is compared with a preset threshold. When the Mahalanobis distance is greater than the preset threshold, it is determined that the battery cell has a micro short circuit fault. For a battery cell that has experienced a micro short circuit fault, the first charging voltage curve of the first discharge and charging cycle and the second charging voltage curve of the second discharge and charging cycle are obtained when the battery cell is subjected to two adjacent discharge and charging cycles. Based on the first charging voltage curve and the second charging voltage curve, the micro-short-circuit current and micro-short-circuit resistance of the battery cell are calculated, and then the degree of failure of the lithium-ion battery pack is evaluated based on the micro-short-circuit current and micro-short-circuit resistance. The Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated using the following formula: ； ； ； in, The Mahalanobis distance between the differential voltage curve and the median differential voltage curve. D The differential voltage curves are for individual battery cells. D med This is the median differential voltage curve for a lithium-ion battery pack. N The lengths of the differential voltage curve and the median differential voltage curve; The covariance matrix between the differential voltage curve and the median differential voltage curve is given.

2. The method for detecting and evaluating gradual faults in lithium batteries as described in claim 1, characterized in that, Before calculating the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, the method further includes: The differential voltage curve and the median differential voltage curve are smoothed according to a preset moving average filter. The moving average filter is: ； in, s r Original signal s In the r The value at time, For the corresponding filtered value, 2 N p +1 represents the window size of the moving average filter. N p It is an integer. l This represents the time lag number. s r-l For the original signal at the 1st r - l The value at time.

3. The method for detecting and evaluating gradual faults in lithium batteries as described in claim 1, characterized in that, The differential voltage for each individual battery cell or the median differential voltage for the lithium-ion battery pack can be calculated using the following formula: ； Where DV is the differential voltage corresponding to each individual battery cell or the median differential voltage corresponding to the lithium-ion battery pack. Q To charge the battery capacity, V This refers to the terminal voltage in constant current charging mode. Q 2 -Q 1 represents the voltage interval Δ V Changes in internal charging capacity.

4. The method for detecting and evaluating gradual faults in lithium batteries as described in claim 1, characterized in that, The step of calculating the micro-short-circuit current and micro-short-circuit resistance of the battery cell based on the first charging voltage curve and the second charging voltage curve includes: Based on the second charging voltage curve, the voltage at the end of the second discharge and charging cycle is obtained, and the voltage is interpolated on the first charging voltage curve. The time obtained after interpolation is taken as the first time. The moment when the first discharge and the charging cycle ends is taken as the second moment, and the time interval between the first moment and the second moment is taken as the remaining charging time. The charging current data during the first discharge and charge cycle and the second discharge and charge cycle are obtained. Based on the remaining charging time and the charging current data, the corresponding remaining charging capacity is calculated. Then, based on the remaining charging capacity, the micro-short-circuit current and micro-short-circuit resistance of the battery cell are calculated.

5. The method for detecting and evaluating gradual faults in lithium batteries as described in claim 4, characterized in that, Calculate the remaining charging capacity using the following formula: ； in, Q Δ For the remaining charging capacity, Δ t For the remaining charging time, This contains charging current data for the first and second discharge and charge cycles.

6. The method for detecting and evaluating gradual faults in lithium batteries as described in claim 5, characterized in that, The micro short-circuit current of the battery cell is calculated using the following formula: ； in, I err This refers to the micro-short-circuit current of a single battery cell. T c The time of one discharge and charge cycle; The micro short-circuit resistance of the battery cell can be calculated using the following formula: ； in, The micro short-circuit resistance of a single battery cell. U This is the average voltage over the discharge and charge cycles.

7. A device for detecting and evaluating gradual faults in lithium batteries, characterized in that, include: Terminal voltage acquisition module, differential voltage calculation module, micro short circuit fault judgment module, charging voltage curve acquisition module, and fault severity assessment module; The terminal voltage acquisition module is used to acquire the terminal voltage of each battery cell in the lithium-ion battery pack, sort the terminal voltage of each battery cell, and take the median of the sorted terminal voltages as the median terminal voltage of the corresponding battery cell. The differential voltage calculation module is used to calculate the differential voltage corresponding to each battery cell based on the terminal voltage and generate the corresponding differential voltage curve; and to calculate the median differential voltage corresponding to the lithium-ion battery pack based on the median terminal voltage and generate the corresponding median differential voltage curve. The micro-short circuit fault detection module is used to calculate the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve for each battery cell, and compare the Mahalanobis distance with a preset threshold. When the Mahalanobis distance is greater than the preset threshold, it is determined that the battery cell has a micro-short circuit fault. The Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated using the following formula: ； ； ； in, The Mahalanobis distance between the differential voltage curve and the median differential voltage curve. D The differential voltage curves are for individual battery cells. D med This is the median differential voltage curve for a lithium-ion battery pack. N The lengths of the differential voltage curve and the median differential voltage curve; The covariance matrix between the differential voltage curve and the median differential voltage curve; The charging voltage curve acquisition module is used to acquire, for a battery cell that has experienced a micro short circuit fault, the first charging voltage curve of the first discharge and charging cycle and the second charging voltage curve of the second discharge and charging cycle when the battery cell is subjected to two adjacent discharge and charging cycles. The fault severity assessment module is used to calculate the micro-short-circuit current and micro-short-circuit resistance of the battery cell based on the first charging voltage curve and the second charging voltage curve, and then assess the fault severity of the lithium-ion battery pack based on the micro-short-circuit current and micro-short-circuit resistance.

8. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the method for detecting and evaluating lithium battery gradual failures as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the storage medium to perform the lithium battery gradual fault detection and evaluation method as described in any one of claims 1 to 6.

Images