Battery diagnosis method and system based on temperature compensation and robust trend decomposition

Abstract

The application relates to the technical field of battery safety monitoring and state diagnosis, and provides a battery diagnosis method and system based on temperature compensation and robust trend decomposition, which comprises the following steps: collecting the cut-off voltage, single battery temperature, running segment duration and current data of a plurality of single batteries in a battery module in a plurality of running segments; calculating the voltage variation of each single battery in each running segment; obtaining the temperature of the i-th single battery in the j-th running segment; obtaining the cumulative time amount and the current influence amount; establishing a fitting model; solving the cumulative time influence coefficient of the fitting model by using a robust regression method, and calculating the cumulative time contribution amount of each single battery; according to the cumulative time contribution amount of each single battery, the deviation value of the cumulative time contribution amount of each single battery is calculated; when the deviation value meets a preset condition, the single battery is identified as a suspected abnormal single battery. In this way, the reliability of the battery diagnosis scheme is improved.

Description

Technical Field

[0001] This application relates to the field of battery safety monitoring and condition diagnosis technology, specifically to a battery diagnosis method and system based on temperature compensation and robust trend decomposition. Background Technology

[0002] With the large-scale application of electrochemical energy storage systems and power battery systems, battery modules are typically composed of a large number of individual cells connected in series, parallel, or series-parallel. Influenced by factors such as manufacturing consistency, electrode condition, internal resistance, temperature field, connection impedance, state of charge, and degree of aging, individual cells will exhibit certain voltage differences during operation. For normal batteries, under the same charge and discharge conditions, their voltage changes are usually similar; when external conditions change, most individual cells tend to rise or fall synchronously.

[0003] When a micro-short circuit, abnormal self-discharge, localized defects, or abnormal degradation of active materials occurs within a single battery cell, the voltage of that cell may gradually deviate from that of other normal cells in the module. Early signs of this anomaly typically manifest as a weak, slow, and cumulative voltage drop, which may not trigger traditional overvoltage, undervoltage, or differential voltage threshold alarms in a short period. If not detected in time, this type of anomaly may further develop into accelerated capacity decay, localized overheating, insulation deterioration, and even an increased risk of thermal runaway.

[0004] Existing battery anomaly identification methods mostly rely on individual cell voltage thresholds, module voltage difference thresholds, fixed statistical thresholds, or manual experience. However, changes in charging and discharging current alter the ohmic voltage drop and polarization voltage in the terminal voltage, temperature changes affect the reaction rate and internal resistance, and the voltage response sensitivity varies across different states of charge ranges. Therefore, directly relying on absolute voltage or simple voltage difference for judgment can easily misinterpret normal overall operating condition fluctuations as individual cell anomalies, and may also cause slowly developing early anomalies to be submerged in overall fluctuations.

[0005] It is evident that the battery anomaly identification schemes in related technologies suffer from poor reliability. Summary of the Invention

[0006] This application aims to address the problem of poor reliability in battery anomaly identification schemes in related technologies, and provides a battery diagnosis method and system based on temperature compensation and robust trend decomposition.

[0007] To solve the above problems, this application is implemented as follows:

[0008] Firstly, this application provides a battery diagnostic method based on temperature compensation and robust trend decomposition, including: Collect data on the cutoff voltage, individual cell temperature, duration of operation segment, and current of multiple individual cells within the battery module during multiple operating segments; Using the target cutoff voltage of each individual cell as a reference, and based on the cutoff voltage of each individual cell in each operating segment, the voltage change of each individual cell in each operating segment is calculated; based on the individual cell temperature of multiple individual cells in multiple operating segments, the first... The single cell in the first Temperature in each running segment ; According to the first The cumulative time is calculated based on the duration of each running segment. and according to the first Acquiring the current impact of current data from each running segment. ; The voltage change is decomposed into a fixed deviation term, a temperature effect term, a current effect term, and a cumulative time effect term. Based on these terms, a single-cell voltage change fitting model is established. The expression for the fitting model is as follows:

[0009] In the formula, For a fixed deviation coefficient, This is the temperature influence coefficient. The current influence coefficient, This is the cumulative time impact coefficient; The parameters of the fitted model are solved using a robust regression method, and the cumulative time contribution of each individual cell is calculated based on the cumulative time amount and the solved cumulative time influence coefficient. Based on the cumulative time contribution of each individual battery cell, the deviation of the cumulative time contribution of each individual battery cell relative to the average cumulative time contribution of all individual batteries cells in the battery module is calculated; when the deviation is less than a preset deviation and the cumulative time contribution of the individual battery cell maintains a consistent negative trend in multiple consecutive operating segments, the individual battery cell is identified as a suspected abnormal individual battery cell.

[0010] Secondly, this application provides a battery diagnostic system based on temperature compensation and robust trend decomposition, comprising: The data acquisition module is used to collect data on the cutoff voltage, individual cell temperature, duration of operation segment, and current of multiple individual cells within the battery module during multiple operating segments. The first acquisition module is used to calculate the voltage change of each individual battery in each operating segment, based on the target cutoff voltage of each individual battery and the cutoff voltage of each individual battery in each operating segment; and to acquire the first... The single cell in the first Temperature in each running segment ; The second acquisition module is used to obtain information based on the first... The cumulative time is calculated based on the duration of each running segment. and according to the first Acquiring the current impact of current data from each running segment. ; A module is established to decompose the voltage change into a fixed deviation term, a temperature effect term, a current effect term, and a cumulative time effect term. Based on these terms, a fitting model for the voltage change of a single battery cell is established. The expression of the fitting model is as follows:

[0011] In the formula, For a fixed deviation coefficient, This is the temperature influence coefficient. The current influence coefficient, This is the cumulative time impact coefficient; The calculation module is used to solve the parameters of the fitted model using a robust regression method, and to calculate the cumulative time contribution of each individual cell based on the cumulative time amount and the calculated cumulative time influence coefficient. The diagnostic module is used to calculate the deviation of the cumulative time contribution of each individual battery cell from the average cumulative time contribution of all individual batteries cells in the battery module, based on the cumulative time contribution of each individual battery cell. When the deviation is less than a preset deviation and the cumulative time contribution of the individual battery cell maintains a consistent negative trend in multiple consecutive operating segments, the individual battery cell is identified as a suspected abnormal individual battery cell.

[0012] Thirdly, this application provides a terminal device including a processor and a memory, wherein the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0013] Fourthly, this application provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0014] Fifthly, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the steps of the method described in the first aspect.

[0015] In a sixth aspect, this application provides a computer program product stored in a storage medium, the program product being executed by at least one processor to implement the steps of the method described in the first aspect.

[0016] Compared with the prior art, this application has the following beneficial effects: By calculating voltage changes based on the target cutoff voltage of each individual battery cell, the interference of differences in the absolute value of the initial voltage of different cells on trend judgment is eliminated. A four-variable fitting model, including a fixed deviation term, a temperature influence term, a current influence term, and a cumulative time influence term, decomposes the voltage change into contributions from different physical factors, effectively separating normal voltage fluctuations caused by temperature and current changes from the sustained voltage drop caused by micro-short circuits. Robust regression is used to solve the model parameters, reducing the impact of data jumps and measurement noise on parameter estimation. By calculating the deviation of the cumulative time contribution of each individual battery cell from the average cumulative time contribution of all individual batteries in the battery module, combined with the dual criterion of maintaining a consistent negative trend across multiple consecutive operating segments, false alarms caused by single fluctuations are effectively avoided. This application does not rely on a single fixed voltage threshold and has high sensitivity for identifying early, weak, and slowly developing micro-short circuits and abnormal self-discharge. It is suitable for online monitoring and offline diagnosis of energy storage battery modules, power battery packs, and secondary battery packs, improving the reliability of battery diagnostic solutions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a battery diagnostic method based on temperature compensation and robust trend decomposition provided in an embodiment of this application. Figure 2 This is a comparison chart of the cumulative time contribution of 12 batteries in the last running segment provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a battery diagnostic system based on temperature compensation and robust trend decomposition provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. 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] The terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Additionally, the use of "and / or" in this application indicates at least one of the connected objects, such as A and / or B and / or C, representing seven possibilities: including A alone, B alone, C alone, and the presence of both A and B, both B and C, both A and C, and the presence of A, B, and C.

[0021] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0022] The battery diagnostic method based on temperature compensation and robust trend decomposition provided in this application will be described below.

[0023] See Figure 1 , Figure 1 This is a schematic flowchart of a battery diagnostic method based on temperature compensation and robust trend decomposition provided in an embodiment of this application. Figure 1 The battery diagnostic method based on temperature compensation and robust trend decomposition shown can be executed by terminal devices such as computers.

[0024] like Figure 1 As shown, the battery diagnostic method based on temperature compensation and robust trend decomposition provided in this application may include the following steps: Step 101: Collect data on the cutoff voltage, individual cell temperature, duration of operation segment, and current of multiple individual cells within the battery module during multiple operating segments.

[0025] In this embodiment, the collected cutoff voltage, cell temperature, operating segment duration, and current data can be plotted into a two-dimensional data table so that users can view and retrieve the corresponding battery parameters.

[0026] In some embodiments, the cutoff voltage may be a charging cutoff voltage, a discharging cutoff voltage, a terminal voltage under a specified state of charge window, a voltage after resting, or other voltage characteristic values ​​that can reflect the differences in the state of a single cell.

[0027] In some embodiments, the operating segment refers to a continuous data interval used for analysis, and its division methods include, but are not limited to: a complete charge-discharge cycle; a complete charging process or a complete discharging process; a continuous operating segment of a preset fixed duration, such as 1 hour, 2 hours, or a data recording cycle; a resting segment or a shelving segment; a continuous sampling segment within a specified state of charge window; a power conversion segment or operating condition switching segment; or any valid analysis interval automatically divided by the battery management system or energy management system according to the operating conditions. That is, the operating segment division methods include, but are not limited to: a complete charge-discharge cycle, a continuous operating segment of a predetermined duration, a complete charging segment, a complete discharging segment, a resting segment, a sampling segment within a specified state of charge window, or a valid analysis interval divided by the battery management system.

[0028] In some embodiments, the rules for dividing each operational segment within the same analysis task remain consistent. In online applications, when operational data is continuously flowing in and has not yet formed a complete cycle, operational segments can be divided into units of predetermined duration, such as 1 hour, to ensure real-time diagnostics. In offline analysis, operational segments can be divided into units of a complete charge-discharge cycle to fully characterize the battery's state changes under complete operating conditions. For daily operational data of energy storage power stations, each day is considered one operational segment; for driving data of electric vehicles, each trip or charging event is considered one operational segment.

[0029] During data acquisition, each data point is timestamped. The acquired data undergoes preprocessing, which includes time sorting, unit standardization, missing value marking, removal of obviously invalid values, and checking the number of valid samples. For long-term continuous missing values ​​caused by communication interruptions, no leapfrog interpolation is performed; for a small number of isolated missing values, adjacent valid values, local medians, or samples from the same operating conditions can be used to fill in the gaps. After preprocessing, subsequent calculations are performed only on individual units and runtime segments that meet the minimum number of valid samples.

[0030] Step 102: Using the target cutoff voltage of each individual cell as a reference, calculate the voltage change of each individual cell in each operating segment; obtain the corresponding temperature.

[0031] In this context, obtaining the corresponding temperature can be understood as obtaining the temperature of a single cell from multiple individual cells in multiple operating segments. The single cell in the first Temperature in each running segment .

[0032] In this embodiment, by using the target cutoff voltage of each individual cell as a benchmark and calculating the voltage change of each individual cell in each operating segment based on the cutoff voltage of the individual cell in each operating segment, the influence of the difference in the absolute value of the initial voltage of different individual cells can be reduced.

[0033] In some embodiments, the target cutoff voltage can be understood as the cutoff voltage of each individual cell in the first operating segment. This refers to the voltage value of the individual cell in the first operating segment of the time series. Using the target cutoff voltage as the baseline for each individual cell, the cutoff voltages of subsequent operating segments are... The voltage change was obtained by comparing it with this benchmark.

[0034] In the formula, Indicates the first The single cell in the first Voltage changes in a single running segment.

[0035] Through the above benchmarking process, the static voltage deviation caused by manufacturing differences, inconsistent initial state of charge, or differences in historical operating conditions of different individual cells can be effectively eliminated, so that the fitting model established in subsequent steps can focus on the relative change trend of each individual cell over time.

[0036] In some embodiments, if the data of the first running segment is found to be missing, jump, or abnormally acquired after validity determination, the cutoff voltage of the first valid running segment can be used as the target cutoff voltage to ensure the reliability of the reference.

[0037] In some embodiments, the temperature For the first The temperature is determined by at least one of the following: surface temperature of a single cell, terminal temperature, casing temperature, or estimated internal temperature; when a single operating segment contains multiple temperature samples, the end temperature, average temperature, median temperature, or highest temperature is used as the temperature. .

[0038] In some embodiments, it is preferable to use the temperature value closest to the sampling time of the cutoff voltage; when the time interval between the two exceeds the allowable threshold, the sample is marked as invalid to avoid temperature and voltage misalignment.

[0039] The allowable threshold can be set to 1 second, 5 seconds, or 10 seconds.

[0040] In some embodiments, if the sampling frequencies of voltage and temperature are different or there is a time offset, the temperature data and cutoff voltage data of each individual cell can be aligned according to the timestamp.

[0041] Specifically, for the first For each operating segment, the temperature sample value that is closest in time to the sampling time of the cutoff voltage is selected as the temperature of that operating segment. Ensure the temperature is substituted into the fitted model. With voltage change It is consistent in time.

[0042] It is understandable that the specific implementation method of time alignment can be flexibly selected by those skilled in the art based on the actual data collection situation.

[0043] Step 103: Obtain the corresponding cumulative time and current influence.

[0044] Specifically, obtaining the corresponding cumulative time and current influence is as follows: based on the first... The cumulative time is calculated based on the duration of each running segment. and according to the first Acquiring the current impact of current data from each running segment. .

[0045] In this embodiment, the cumulative time amount Used to characterize the cumulative extent of battery operation across various segments, and the cumulative time. It is obtained by summing the durations of each running segment in chronological order:

[0046] In the formula, Indicates the first The duration of each running segment.

[0047] When the running segment is of a predetermined duration For a preset fixed value, such as 1 hour, the cumulative time amount Linearly related to the running segment number; when the running segment is a charge / discharge cycle, The actual duration of each cycle is represented by the cumulative time, which reflects the actual operating time of the battery. Both methods can effectively describe the cumulative effect of micro-short circuits over operating time.

[0048] In some embodiments, when runtime duration data is missing, runtime segment number, cumulative energy, or cumulative runtime can be used instead of the cumulative time. For example, when the durations of all runtime segments are approximately equal, the runtime segment number can be directly used as a measure of the cumulative extent.

[0049] In some embodiments, the current influence amount It can be the first The average current, maximum current, minimum current, or current at a specified sampling time within a given operating segment. Preferably, for the charging process, the average charging current is used; for the discharging process, the average discharging current is used; for operating segments involving mixed charging and discharging conditions, the effective or absolute average value of the current is used. When the object of analysis is a static self-discharge process, the average current can be used... Set it to zero.

[0050] Step 104: Establish a fitting model for the voltage change of a single cell.

[0051] In this embodiment, the voltage change can be decomposed into a fixed deviation term, a temperature influence term, a current influence term, and a cumulative time influence term. A single-cell voltage change fitting model is established based on the fixed deviation term, the temperature influence term, the current influence term, and the cumulative time influence term. The expression of the fitting model is:

[0052] In the formula, For a fixed deviation coefficient, This is the temperature influence coefficient. The current influence coefficient, This represents the cumulative time impact coefficient.

[0053] Among them, the fixed deviation coefficient is used to absorb constant shifts caused by other factors not considered in the absorption model; the temperature influence coefficient is used to describe the first... The internal resistance, polarization, and cutoff voltage shift caused by the temperature change of a single cell; the current influence coefficient is used to describe the terminal voltage shift caused by current changes; the cumulative time influence coefficient is used to capture the continuous deviation trend of the single cell accumulated over a longer time scale with operating time.

[0054] In this embodiment, the temperature effect item Used to compensate for normal voltage changes caused by temperature; current effect item Used to compensate for changes in terminal voltage caused by changes in current; cumulative time effect item Used to extract the signal of continuous cumulative voltage drop caused by micro-short circuits or abnormal self-discharge.

[0055] In some embodiments, calculations can be performed separately first. , and The effective sample size, range, variance, or robustness scale is used. When a variable is missing, has insufficient effective samples (e.g., fewer than 10 effective samples), has no effective fluctuations (e.g., range less than a preset threshold), or is approximately constant, the variable is removed from the feature matrix, and the corresponding coefficient is set to zero. By automatically reducing the order based on variable effectiveness, ill-conditioned feature matrices, non-unique parameters, or abnormally amplified parameter values ​​can be avoided.

[0056] Step 105: Solve the parameters of the fitted model using the robust regression method, and calculate the cumulative time contribution of each individual cell based on the cumulative time amount and the calculated cumulative time influence coefficient.

[0057] In this embodiment, by using the robust regression method to solve the parameters of the fitted model, the impact of jump points, noise, and a small number of outlier samples on the fitting results can be reduced.

[0058] For the Each individual cell has a parameter vector. and the Feature vectors of each running segment They are respectively:

[0059]

[0060] Among them, residual It can be represented as:

[0061] Objective of parameter estimation for robust regression for:

[0062] Robust loss function for:

[0063] In the formula, The robustness coefficient is denoted as .

[0064] In some embodiments, initial parameters can be obtained using ordinary least squares, the robust scale can be estimated based on the median absolute deviation of the residuals, and the sample weights can be updated according to the residual size.

[0065]

[0066] In the formula, For the first The weights of each sample, The robustness coefficient is... For residual robustness, The median absolute deviation of the residuals. To prevent small positive quantities with a denominator of zero, the weighted least squares problem is then solved and the parameters are updated until the parameter changes between two consecutive iterations are less than a preset convergence threshold, or the maximum number of iterations is reached.

[0067] In some embodiments, robustness coefficient Generally, 1.345 or 1.5 is used, but it can be adjusted within the range of 1.0 to 2.0 depending on the actual data quality. The smaller the value, the stronger the suppression of outliers, but it may sacrifice the efficiency of valid data. The larger the value, the closer it is to ordinary least squares.

[0068] In this embodiment, robust regression is used to apply quadratic loss when the residuals are small, assigning full weight to the samples; and linear loss is used when the residuals are large, reducing the weight of the corresponding samples. This effectively reduces the impact of occasional jumps, communication noise, and a small number of outlier samples on the estimation of model parameters.

[0069] After fitting, the first step is calculated based on the model parameters. The single cell in the first Fitted voltage of each running segment and residuals :

[0070]

[0071] And further calculate the temperature contribution. Cumulative time contribution and current contribution :

[0072]

[0073]

[0074] Among them, temperature contribution Used to quantify the contribution of temperature change to voltage change; cumulative time contribution. Used to quantify the contribution of cumulative running time to voltage change; current contribution. Used to quantify the contribution of current change to voltage change.

[0075] It should be noted that if a single cell is in the last operating segment... The significantly negative value indicates that even after deducting the effects of temperature and current, the cell still exhibits an independent decreasing trend over time, which is a typical characteristic of micro-short circuits or abnormal self-discharge.

[0076] Step 106: Based on the cumulative time contribution of each individual battery cell, calculate the deviation of the cumulative time contribution of each individual battery cell relative to the average cumulative time contribution of all individual batteries cells in the battery module; when the deviation is less than a preset deviation and the cumulative time contribution of the individual battery cell maintains a consistent negative trend in multiple consecutive operating segments, identify the individual battery cell as a suspected abnormal individual battery cell.

[0077] In some embodiments, the deviation of the cumulative time contribution of each individual battery cell from the average cumulative time contribution of all individual batteries in the battery module can be calculated based on the cumulative time contribution of each individual battery cell. In other words, the deviation of the cumulative time contribution of each individual battery cell from the median of the cumulative time contribution of all individual batteries in the battery module can be calculated. That is, the average cumulative time contribution of all individual batteries in the battery module can be equivalent to the median of the cumulative time contribution of all individual batteries in the battery module.

[0078] In some embodiments, it is possible to Perform robust standardization:

[0079] In the formula, For the first The cumulative time contribution of each individual cell in the last operating segment; It is the average cumulative time contribution of all individual cells in the battery module, or the median of the cumulative time contribution of all individual cells in the battery module. Median absolute deviation of the cumulative time contribution of a single cell; For positive small quantities, such as This is used to prevent the denominator from being zero.

[0080] The deviation value is the robust standardized value mentioned above. ,when A negative value and the larger the absolute value, the more negative the cumulative time contribution of that single cell is relative to other cells in the module.

[0081] In some embodiments, a preset deviation value is set. ,when At that time, it was initially determined that the single battery cell posed an abnormal risk.

[0082] Furthermore, to avoid false alarms caused by a single jump point, the cumulative time contribution of the single cell is required to maintain a consistent negative trend across multiple consecutive operating segments (e.g., 3 or 5 consecutive operating segments).

[0083] Specifically, the monotonicity of the cumulative time contribution can be checked. If the cumulative time contribution continues to decrease in multiple consecutive running segments, then the trend is confirmed to be consistent.

[0084] In some embodiments, a single cell is identified as a suspected abnormal single cell when both of the following conditions are met: Deviation value Less than the preset deviation value ; The cumulative time contribution of this single cell showed a consistent negative trend across multiple consecutive operating segments.

[0085] In this embodiment, the false alarm rate can be effectively reduced and the reliability of the identification results can be ensured by using the above dual criteria.

[0086] In some embodiments, the preset deviation value is obtained by setting at least one of battery type, module capacity, operating conditions, or service life; or by statistically calibrating the cumulative time contribution deviation value of a known normal battery module.

[0087] In some embodiments, an adaptive determination method may be used:

[0088] In the formula, This is a preset positive coefficient, typically a positive number between 2 and 4, preferably 2.5 or 3. The value of can be adjusted according to the desired detection sensitivity and false alarm rate: The smaller the value, the higher the detection sensitivity, but the false alarm rate also increases accordingly. The higher the value, the lower the false alarm rate, but it may miss early weak anomalies.

[0089] In some embodiments, the preset deviation value is calculated offline based on the data from the first few operating segments during the calibration phase of the initial operation of the battery module and stored in the battery management system. It remains unchanged in subsequent operations or is recalibrated after the individual cells in the module are replaced or balanced.

[0090] In some embodiments, after identifying suspected abnormal individual cells, the individual cells can be comprehensively sorted and the sorting results can be output to assist maintenance personnel in determining the priority of anomaly investigation.

[0091] In some embodiments, the battery diagnostic method based on temperature compensation and robust trend decomposition provided in this application can be applied to a battery module consisting of 12 280 Ah batteries connected in series, numbered #1 to #12. A 100 Ω resistor is connected in parallel across battery #4 to simulate a micro-short circuit inside the battery. The cutoff voltage of each individual battery is recorded during eight consecutive operating segments of the module. Current and temperature And calculate the cumulative time contribution of each individual cell in the last operating segment according to the above steps. The calculation results are as follows: Figure 2 As shown, battery #4 Reduced to approximately 14 mV, significantly lower than the other 11 batteries. The voltage drop of 3–3 mV indicates that even after accounting for the effects of temperature and current, battery #4 still exhibits a significant cumulative voltage decrease trend. Therefore, battery #4 can be identified as a suspected micro-short-circuited battery, consistent with the preset fault location, thus verifying the ability of this method to identify micro-short-circuited batteries.

[0092] This application provides a battery diagnostic method based on temperature compensation and robust trend decomposition. By calculating voltage changes using the target cutoff voltage of each individual cell as a benchmark, it eliminates the interference of differences in the absolute value of the initial voltage of different cells on trend judgment. Through establishing a four-variable fitting model including a fixed deviation term, a temperature influence term, a current influence term, and a cumulative time influence term, it decomposes the voltage change into contributions from different physical factors, effectively separating normal voltage fluctuations caused by temperature and current changes from the continuous voltage drop caused by micro-short circuits. By employing a robust regression method to solve the model parameters, it reduces the impact of data jumps and measurement noise on parameter estimation. By calculating the deviation of the cumulative time contribution of each individual cell from the average cumulative time contribution of all individual cells in the battery module, combined with the dual criterion of maintaining a consistent negative trend across multiple consecutive operating segments, it effectively avoids false alarms caused by single fluctuations. This application does not rely on a single fixed voltage threshold and has high sensitivity for identifying early, weak, and slowly developing micro-short circuits and abnormal self-discharge. It is suitable for online monitoring and offline diagnosis of energy storage battery modules, power battery packs, and secondary battery packs, improving the reliability of battery diagnostic solutions.

[0093] See Figure 3 , Figure 3 This is a schematic diagram of the structure of a battery diagnostic system based on temperature compensation and robust trend decomposition provided in an embodiment of this application. Figure 3 As shown, system 300 includes: The acquisition module 301 is used to acquire the cutoff voltage, individual cell temperature, duration of operation segment and current data of multiple individual cells in the battery module in multiple operating segments; The first acquisition module 302 is used to calculate the voltage change of each individual battery in each operating segment based on the target cutoff voltage of each individual battery and the cutoff voltage of each individual battery in each operating segment; and to acquire the first individual battery temperature in multiple operating segments based on the individual battery temperature in multiple operating segments. The single cell in the first Temperature in each running segment ; The second acquisition module 303 is used to obtain information according to the first... The cumulative time is calculated based on the duration of each running segment. and according to the first Acquiring the current impact of current data from each running segment. ; Module 304 is established to decompose the voltage change into a fixed deviation term, a temperature influence term, a current influence term, and a cumulative time influence term. Based on the fixed deviation term, the temperature influence term, the current influence term, and the cumulative time influence term, a fitting model for the voltage change of a single battery cell is established. The expression of the fitting model is:

[0094] In the formula, For a fixed deviation coefficient, This is the temperature influence coefficient. The current influence coefficient, This is the cumulative time impact coefficient; The calculation module 305 is used to solve the parameters of the fitted model using a robust regression method, and to calculate the cumulative time contribution of each individual cell based on the cumulative time amount and the solved cumulative time influence coefficient. The diagnostic module 306 is used to calculate the deviation of the cumulative time contribution of each individual battery cell relative to the average cumulative time contribution of all individual batteries cells in the battery module based on the cumulative time contribution of each individual battery cell; when the deviation is less than a preset deviation and the cumulative time contribution of the individual battery cell maintains a consistent negative trend in multiple consecutive operating segments, the individual battery cell is identified as a suspected abnormal individual battery cell.

[0095] Optionally, the temperature For the first The temperature is determined by at least one of the following: surface temperature of a single cell, terminal temperature, casing temperature, or estimated internal temperature; when a single operating segment contains multiple temperature samples, the end temperature, average temperature, median temperature, or highest temperature is used as the temperature. .

[0096] Optionally, the operating segment includes a complete charge-discharge cycle, a continuous operating segment of a predetermined duration, a complete charging segment, a complete discharging segment, a resting segment, a sampling segment under a specified state of charge window, or an effective analysis interval divided by the battery management system.

[0097] Optionally, the preset deviation value is obtained by setting at least one of the following: battery type, module capacity, operating conditions, or service life; or by statistically calibrating the cumulative time contribution deviation value of a known normal battery module.

[0098] The battery diagnostic system based on temperature compensation and robust trend decomposition provided in this application can achieve the functions described in this application. Figure 1 The various processes in the method embodiments, and the ways to achieve the same beneficial effects, will not be repeated here to avoid repetition.

[0099] like Figure 4 As shown, this application also provides a terminal device, including a processor 401 and a memory 402. The memory 402 stores a program or instructions that can run on the processor 401. When the program or instructions are executed by the processor 401, they implement the various steps of the above-described battery diagnosis method embodiment based on temperature compensation and robust trend decomposition, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0100] It should be noted that the terminal device in this application can be a terminal or other devices besides a terminal. For example, the terminal device can be a tablet computer, a laptop computer, etc., and this application does not make any specific limitation.

[0101] This application also provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes of the above-described battery diagnostic method embodiments based on temperature compensation and robust trend decomposition, and achieve the same technical effect. To avoid repetition, these will not be described again here.

[0102] The processor is the processor in the terminal device described in the above embodiments. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (Read-Only Memory). Only memory (ROM), random access memory (RAM), magnetic disks or optical disks, etc.

[0103] This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described battery diagnostic method embodiments based on temperature compensation and robust trend decomposition, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0104] It should be understood that the chip mentioned in this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0105] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the battery diagnostic method embodiment based on temperature compensation and robust trend decomposition described above, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0106] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as a read-only memory). The device includes a number of instructions in a ROM (random access memory), RAM (magnetic disk), or optical disk to cause a terminal (which may be a computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0108] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A battery diagnostic method based on temperature compensation and robust trend decomposition, characterized in that, include: Collect data on the cutoff voltage, individual cell temperature, duration of operation segment, and current of multiple individual cells within the battery module during multiple operating segments; Using the target cutoff voltage of each individual cell as a reference, and based on the cutoff voltage of each individual cell in each operating segment, the voltage change of each individual cell in each operating segment is calculated; Based on the individual cell temperatures of multiple cells in multiple operating segments, the first... The single cell is in the first Temperature in each running segment ; According to the first The cumulative time is calculated based on the duration of each running segment. and according to the first Acquiring the current impact of current data from each running segment. ; The voltage change is decomposed into a fixed deviation term, a temperature effect term, a current effect term, and a cumulative time effect term. Based on these terms, a single-cell voltage change fitting model is established. The expression for the fitting model is as follows: In the formula, For a fixed deviation coefficient, This is the temperature influence coefficient. The current influence coefficient, This is the cumulative time impact coefficient; The parameters of the fitted model are solved using a robust regression method, and the cumulative time contribution of each individual cell is calculated based on the cumulative time amount and the solved cumulative time influence coefficient. Based on the cumulative time contribution of each individual battery cell, calculate the deviation of the cumulative time contribution of each individual battery cell from the average cumulative time contribution of all individual batteries cells in the battery module. When the deviation value is less than the preset deviation value and the cumulative time contribution of the single cell maintains a consistent negative trend in multiple consecutive operating segments, the single cell is identified as a suspected abnormal single cell.

2. The method according to claim 1, characterized in that, The temperature For the first The temperature is determined by at least one of the following: surface temperature of a single cell, terminal temperature, casing temperature, or estimated internal temperature; when a single operating segment contains multiple temperature samples, the end temperature, average temperature, median temperature, or highest temperature is used as the temperature. .

3. The method according to claim 1, characterized in that, The operating segments include complete charge-discharge cycles, continuous operating segments of predetermined duration, complete charging segments, complete discharging segments, resting segments, sampling segments under specified state of charge windows, or effective analysis intervals divided by the battery management system.

4. The method according to any one of claims 1 to 3, characterized in that, The preset deviation value is obtained by setting at least one of the following: battery type, module capacity, operating conditions, or service life; or by statistically calibrating the cumulative time contribution deviation value of a known normal battery module.

5. A battery diagnostic system based on temperature compensation and robust trend decomposition, characterized in that, include: The data acquisition module is used to collect data on the cutoff voltage, individual cell temperature, duration of operation segment, and current of multiple individual cells within the battery module during multiple operating segments. The first acquisition module is used to calculate the voltage change of each individual battery in each operating segment based on the target cutoff voltage of each individual battery and the cutoff voltage of each individual battery in each operating segment. Based on the individual cell temperatures of multiple cells in multiple operating segments, the first... The single cell is in the first Temperature in each running segment ; The second acquisition module is used to obtain information based on the first... The cumulative time is calculated based on the duration of each running segment. and according to the first Acquiring the current impact of current data from each running segment. ; A module is established to decompose the voltage change into a fixed deviation term, a temperature effect term, a current effect term, and a cumulative time effect term. Based on these terms, a fitting model for the voltage change of a single battery cell is established. The expression of the fitting model is as follows: In the formula, For a fixed deviation coefficient, This is the temperature influence coefficient. The current influence coefficient, This is the cumulative time impact coefficient; The calculation module is used to solve the parameters of the fitted model using a robust regression method, and to calculate the cumulative time contribution of each individual cell based on the cumulative time amount and the calculated cumulative time influence coefficient. The diagnostic module is used to calculate the deviation of the cumulative time contribution of each individual battery cell from the average cumulative time contribution of all individual batteries cells in the battery module, based on the cumulative time contribution of each individual battery cell. When the deviation value is less than the preset deviation value and the cumulative time contribution of the single cell maintains a consistent negative trend in multiple consecutive operating segments, the single cell is identified as a suspected abnormal single cell.

6. The system according to claim 5, characterized in that, The temperature For the first The temperature is determined by at least one of the following: surface temperature of a single cell, terminal temperature, casing temperature, or estimated internal temperature; when a single operating segment contains multiple temperature samples, the end temperature, average temperature, median temperature, or highest temperature is used as the temperature. .

7. The system according to claim 5, characterized in that, The operating segments include complete charge-discharge cycles, continuous operating segments of predetermined duration, complete charging segments, complete discharging segments, resting segments, sampling segments under specified state of charge windows, or effective analysis intervals divided by the battery management system.

8. The system according to any one of claims 5 to 7, characterized in that, The preset deviation value is obtained by setting at least one of the following: battery type, module capacity, operating conditions, or service life; or by statistically calibrating the cumulative time contribution deviation value of a known normal battery module.

9. A terminal device, characterized in that, It includes a processor and a memory, wherein the memory stores a program or instructions executable on the processor, which, when executed by the processor, implement the steps of the method as described in any one of claims 1 to 4.

10. A computer program product, characterized in that, The program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the method as described in any one of claims 1 to 4.

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