Abnormality determination method and device of battery pack and electronic equipment

Abstract

The invention discloses a battery pack abnormity determination method and apparatus, and an electronic device. The method comprises the following steps: acquiring operation data of a vehicle-mounted battery pack; performing working condition division on the operation data to obtain corresponding working condition operation data under the charging working condition; according to the working condition operation data, voltage fluctuation values between adjacent data frames under the charging working condition are determined, and the adjacent data frames represent data frames corresponding to two continuous sampling moments arranged according to a time sequence; determining a voltage fluctuation value corresponding to a target state of charge (SOC) interval according to the voltage fluctuation value between the corresponding adjacent data frames; and determining an abnormal result of whether the vehicle-mounted battery pack is abnormal or not according to the voltage fluctuation value of the corresponding target SOC interval under the charging working condition. According to the invention, the technical problem that the determined abnormal result is inaccurate when the battery pack is determined to be abnormal in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of batteries, and more specifically, to a method, apparatus, and electronic device for determining anomalies in a battery pack. Background Technology

[0002] In recent years, the global new energy industry has experienced explosive growth. In terms of technology, lithium-ion batteries remain the mainstream choice, but the technology landscape is becoming increasingly diversified. While new battery technologies such as solid-state batteries and sodium-ion batteries have achieved phased results, large-scale commercial application still requires time. Lithium iron phosphate (LFP) batteries, with their advantages of high safety, long cycle life, and low cost, are rapidly gaining market share.

[0003] In the field of new energy vehicles, especially for lithium iron phosphate battery packs, accurately identifying abnormal states of the battery pack under different operating conditions is a major challenge for battery management systems. Traditional anomaly detection methods are often limited, resulting in low accuracy of anomaly results, especially at various stages when battery performance begins to show subtle deviations.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for determining battery pack anomalies, in order to at least solve the technical problem in the related art where the anomaly determination results are inaccurate when determining battery pack anomalies.

[0006] According to one aspect of the present invention, a method for determining anomalies in a battery pack is provided, comprising: acquiring operational data of an on-board battery pack; dividing the operational data into operating conditions to obtain corresponding operating condition operational data under charging conditions; determining, based on the operating condition operational data, voltage fluctuation values ​​between adjacent data frames under charging conditions, wherein adjacent data frames represent data frames corresponding to two consecutive sampling times arranged in chronological order; determining voltage fluctuation values ​​for a corresponding target state of charge (SOC) interval based on the voltage fluctuation values ​​between corresponding adjacent data frames; and determining an anomaly result indicating whether the on-board battery pack is abnormal based on the voltage fluctuation values ​​for the corresponding target SOC interval under charging conditions.

[0007] Optionally, based on the voltage fluctuation values ​​corresponding to the target SOC range under multiple operating conditions, the abnormal result of determining whether the vehicle battery pack is abnormal is determined, including: based on the voltage fluctuation values ​​corresponding to the target SOC range under charging conditions, the abnormal result of determining whether the vehicle battery pack is abnormal is determined, including: based on the voltage fluctuation values ​​corresponding to the target SOC range, determining the abnormal index corresponding to each target SOC range, wherein the abnormal index includes at least one of the following: range, standard deviation; determining the abnormal threshold corresponding to each target SOC range under charging conditions; and determining the abnormal result based on the abnormal index and abnormal threshold corresponding to each target SOC range under charging conditions.

[0008] Optionally, based on the voltage fluctuation value corresponding to the target SOC range under charging conditions, an abnormal result is determined as to whether the vehicle battery pack is abnormal, including: when the vehicle battery pack includes multiple battery modules, for each battery module, determining the voltage fluctuation value corresponding to the target SOC range under charging conditions; and based on the voltage fluctuation value corresponding to the target SOC range under charging conditions for each battery module, determining sub-abnormal results corresponding to each of the multiple battery modules.

[0009] Optionally, based on the voltage fluctuation value corresponding to the target SOC range under charging conditions, the abnormal result of whether the vehicle battery pack is abnormal is determined, including: if the abnormal result includes a consistency abnormal result, based on the voltage fluctuation value corresponding to the target SOC range under charging conditions for each battery module, a consistency index among multiple battery modules is determined; if the consistency index is lower than the consistency threshold, it is determined that the vehicle battery pack has a consistency abnormal result.

[0010] Optionally, based on the voltage fluctuation value of each battery module in the target SOC range under charging conditions, a consistency index among multiple battery modules is determined, including: for each battery module, based on the voltage fluctuation value in the target SOC range under charging conditions, determining the corresponding voltage fluctuation sum within a preset time period; based on the corresponding voltage fluctuation sum, determining the mean of the corresponding voltage fluctuation sum and the standard deviation of the corresponding voltage fluctuation sum; determining the difference between the corresponding voltage fluctuation sum and the corresponding mean; determining the ratio of the difference to the corresponding standard deviation; and determining the consistency index among multiple battery modules based on the ratios corresponding to each of the multiple battery modules.

[0011] Optionally, based on the voltage fluctuation value corresponding to the target SOC range under the charging condition, an abnormal result is determined as to whether the vehicle battery pack is abnormal, including: if the abnormal result includes abnormal operation of the charging condition, determining the voltage correlation coefficient corresponding to the charging condition; based on the voltage fluctuation value corresponding to the target SOC range under the charging condition and the voltage correlation coefficient, determining the operating condition voltage difference index corresponding to the vehicle battery pack; if the operating condition voltage difference index is greater than a predetermined difference threshold, determining that the vehicle battery pack has an abnormal operating condition.

[0012] Optionally, the voltage fluctuation value of the corresponding target SOC interval is determined based on the voltage fluctuation value between corresponding adjacent data frames, including: obtaining the battery characteristic parameters of the vehicle battery pack; determining the target SOC interval for each operating condition based on the battery characteristic parameters, wherein the corresponding target SOC interval is the state interval where the probability of an anomaly is greater than a predetermined probability of an anomaly under the corresponding operating condition; and determining the voltage fluctuation value of the corresponding target SOC interval based on the voltage fluctuation value between corresponding adjacent data frames.

[0013] According to one aspect of the present invention, a battery pack anomaly determination device is provided, comprising: an acquisition module for acquiring operating data of an on-board battery pack; a first determination module for dividing the operating data into operating conditions to obtain operating condition operating data corresponding to a charging operating condition; a second determination module for determining, based on the operating condition operating data, the voltage fluctuation value between adjacent data frames under the charging operating condition, wherein adjacent data frames represent data frames corresponding to two consecutive sampling times arranged in chronological order; a third determination module for determining, based on the voltage fluctuation value between corresponding adjacent data frames, the voltage fluctuation value of a corresponding target state of charge (SOC) interval; and a fourth determination module for determining, based on the voltage fluctuation value of the corresponding target SOC interval under the charging operating condition, an anomaly result indicating whether the on-board battery pack is abnormal.

[0014] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the battery pack anomaly determination method described in any of the preceding claims.

[0015] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the battery pack anomaly determination method described in any of the preceding claims.

[0016] In this embodiment of the invention, operational data of the vehicle battery pack is acquired; the operational data is divided into operating conditions to obtain corresponding operating condition data under charging conditions; based on the operating condition data, the voltage fluctuation value between adjacent data frames under charging conditions is determined, wherein adjacent data frames represent data frames corresponding to two consecutive sampling times arranged in chronological order; based on the voltage fluctuation value between corresponding adjacent data frames, the voltage fluctuation value of the corresponding target state of charge (SOC) interval is determined; based on the voltage fluctuation value of the corresponding target SOC interval under charging conditions, an abnormal result indicating whether the vehicle battery pack is abnormal is determined. By focusing on the analysis of key SOC intervals under charging conditions, and extracting voltage fluctuation data during the charging process, and centrally analyzing the target SOC interval sensitive to battery performance, the aim of improving the pertinence and reliability of anomaly detection is achieved. Compared with the limitations of traditional methods in terms of application scope or analysis dimensions, this invention, by analyzing the voltage fluctuation characteristics of specific SOC intervals during charging, can more sensitively capture early subtle deviations in battery performance, thereby effectively determining whether the battery pack is abnormal, and thus solving the technical problem in related technologies where the anomaly determination result is inaccurate when determining battery pack abnormality. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a battery pack anomaly determination method according to an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of the SOC-OCV curve in the battery pack consistency estimation method in related technologies;

[0020] Figure 3 This is a schematic diagram of the charging curves of normal and abnormal modules in a consistency estimation method for vehicle-mounted lithium iron phosphate battery packs provided by an optional embodiment of the present invention.

[0021] Figure 4 This is a schematic diagram showing the distribution of voltage fluctuation abnormality of normal and abnormal modules under different SOC states in a consistency estimation method for vehicle-mounted lithium iron phosphate battery packs provided by an optional embodiment of the present invention.

[0022] Figure 5 This is a structural block diagram of a battery pack anomaly determination device according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises 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 such processes, methods, products, or apparatus.

[0025] Example 1

[0026] According to an embodiment of the present invention, an embodiment of a method for determining anomalies in a battery pack is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 This is a flowchart of a battery pack anomaly determination method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0028] Step S102: Obtain the operating data of the vehicle battery pack;

[0029] In step S102 of this application, the operating data of the vehicle battery pack is obtained.

[0030] This includes vehicle battery packs, which are battery packs composed of multiple battery modules connected in series or parallel, used to provide power or electricity to vehicles.

[0031] This involves operational data, which refers to real-time monitoring data generated by the vehicle battery pack during actual operation, including but not limited to battery voltage, current, temperature, SOC (state of charge), individual cell voltage, total battery pack voltage, battery pack temperature, charging status, discharging status, and operating time.

[0032] This step collects various parameters of the battery during operation through onboard sensors and monitoring systems, providing a foundation for subsequent operating condition classification and data processing.

[0033] Step S104: Divide the operating data into operating conditions to obtain the corresponding operating data under the charging condition.

[0034] In step S104 of this application, the corresponding operating data under the charging conditions are obtained.

[0035] This involves operating conditions, which refer to the vehicle's state under different operating conditions. These conditions can include charging, discharging, and stationary conditions, as well as acceleration and emergency braking conditions.

[0036] This involves operating condition data, which specifically refers to a subset of operating data belonging to a particular operating condition after the operating conditions have been divided. This subset of data eliminates data interference from other operating conditions, making the analysis of battery behavior under this specific condition more focused and pure.

[0037] This step categorizes the acquired operational data according to the vehicle's operating conditions (such as charging, discharging, or resting). This categorization helps in subsequent analysis to more accurately identify the battery's performance under specific operating conditions and the patterns of voltage fluctuations.

[0038] Step S106: Based on the operating data, determine the voltage fluctuation value between adjacent data frames under the charging condition, wherein adjacent data frames refer to the data frames corresponding to two consecutive sampling times arranged in chronological order.

[0039] In step S106 of this application, based on the charging operation data extracted in step S104, the voltage change of each battery module in the data record corresponding to every two consecutive sampling times is calculated in time order, thereby obtaining a series of fluctuation values ​​reflecting the instantaneous dynamics of the voltage of each module during the charging process.

[0040] This involves data frames, which are data sets at a specific sampling point in time series data and may include information such as battery voltage and current.

[0041] This includes voltage fluctuation values, which are the changes in battery voltage between two consecutive data frames, reflecting the instantaneous performance differences of the battery under specific operating conditions.

[0042] Voltage fluctuation values ​​are calculated by comparing the difference in battery voltage between two consecutive data frames. This step processes data separately under charging conditions to capture battery performance changes under various conditions.

[0043] Step S108: Determine the voltage fluctuation value of the corresponding target state of charge (SOC) interval based on the voltage fluctuation value between adjacent data frames.

[0044] In step S108 provided in this application, the voltage fluctuation value corresponding to the target SOC range is determined.

[0045] This involves the target SOC range, which is one or more selected ranges within the battery's state of charge. Within these ranges, the battery's performance is particularly sensitive to changes, or the probability of abnormal events occurring is relatively high, making it a key point for battery anomaly detection and performance evaluation.

[0046] This step focuses on identifying the data corresponding to the SOC range where anomalies are easily apparent in battery performance evaluation, and further analyzing voltage fluctuation values ​​in order to detect performance changes of the battery under these critical conditions.

[0047] By focusing on key SOC ranges, the ability to detect battery anomalies can be enhanced, improving diagnostic accuracy. This focus on sensitive SOC ranges makes performance evaluation more targeted and helps to assess battery consistency more accurately.

[0048] Step S110: Based on the voltage fluctuation value corresponding to the target SOC range under charging conditions, determine whether the vehicle battery pack is abnormal.

[0049] In step S110 provided in this application, an abnormal result is finally determined as to whether the vehicle battery is abnormal.

[0050] This includes abnormal results, which are determined based on the voltage fluctuation values ​​within the target SOC range under charging conditions to determine whether the battery pack is in an abnormal state. If the voltage fluctuation values ​​are found to be outside the normal range, the battery pack is considered to be abnormal.

[0051] Because this abnormal result is based on a comprehensive analysis of multiple operating conditions and key SOC intervals, the obtained abnormal result is more reliable and accurate.

[0052] Through steps S102-S110 above, the operating data of the vehicle battery pack is acquired; the operating data is divided into operating conditions to obtain the corresponding operating condition data under the charging condition; based on the operating condition data, the voltage fluctuation value between adjacent data frames under the charging condition is determined, where adjacent data frames represent data frames corresponding to two consecutive sampling times arranged in chronological order; based on the voltage fluctuation value between corresponding adjacent data frames, the voltage fluctuation value of the corresponding target state of charge (SOC) interval is determined; based on the voltage fluctuation value of the corresponding target SOC interval under the charging condition, an anomaly result indicating whether the vehicle battery pack is abnormal is determined. By focusing on the analysis of key SOC intervals under the charging condition, and extracting voltage fluctuation data during the charging process, and centrally analyzing the target SOC interval sensitive to battery performance, the aim of improving the pertinence and reliability of anomaly detection is achieved. Compared to the limitations of traditional methods in terms of application scope or analysis dimensions, this invention, by analyzing the voltage fluctuation characteristics of a specific SOC range during charging, can more sensitively capture early subtle deviations in battery performance, thereby effectively determining whether the battery pack is abnormal. This solves the technical problem in related technologies where the abnormality determination results are inaccurate when determining that the battery pack is abnormal.

[0053] As an optional embodiment, determining whether the vehicle battery pack is abnormal based on the voltage fluctuation value corresponding to the target SOC range under charging conditions includes: determining whether the vehicle battery pack is abnormal based on the voltage fluctuation value corresponding to the target SOC range under charging conditions, including: determining the abnormality index corresponding to each target SOC range based on the voltage fluctuation value, wherein the abnormality index includes at least one of the following: range, standard deviation; determining the abnormality threshold corresponding to each target SOC range under charging conditions; and determining the abnormality result based on the abnormality index and abnormality threshold corresponding to each target SOC range under charging conditions.

[0054] In this embodiment, the steps for determining abnormal results are described.

[0055] This involves anomaly indices, which are indicators used to quantify whether voltage fluctuations deviate from the normal range, including but not limited to range and standard deviation. The range is the difference between the maximum and minimum values ​​in a set of data, while the standard deviation is a statistical measure of the dispersion of a data set.

[0056] This involves an abnormal threshold, which is a pre-set limit value for voltage fluctuation or an abnormal index, used to determine whether the voltage fluctuation of the battery module exceeds the normal range, thereby identifying abnormal states.

[0057] In this step, firstly, based on the voltage fluctuation values ​​within the target SOC range under the charging condition, an anomaly index, such as range or standard deviation, is calculated for each target SOC range. This is to quantify the dispersion of voltage fluctuations within a specific SOC range under the charging condition, in order to identify potential performance deviations. After determining the anomaly index, an anomaly threshold needs to be set for each target SOC range under the charging condition. This threshold is set based on historical data, battery specifications, or expert experience, and is used to determine whether the anomaly index indicates a true anomaly. By comparing the anomaly index corresponding to each target SOC range under the charging condition with the anomaly threshold, it can be determined whether the battery module exhibits abnormal behavior under that charging condition and specific SOC range. Finally, based on the comparison results of the anomaly index and threshold for each target SOC range under the charging condition, it is determined whether the battery pack as a whole is abnormal. If the anomaly index exceeds the corresponding anomaly threshold in one or more target SOC ranges under the charging condition, the battery pack can be determined to be abnormal; otherwise, the battery pack is considered to be in a normal state.

[0058] This method, by focusing on detailed analysis of voltage fluctuations within the target SOC range under charging conditions, can more accurately identify abnormal states of the battery pack, avoiding interference and misjudgments that may arise from differences in the characteristics of data analyzed under different operating conditions or from data in non-sensitive SOC ranges. Furthermore, the determination of abnormal results is based on real-time charging operation data and changes in specific SOC ranges, enabling timely responses to changes in battery status during critical charging phases and providing immediate maintenance recommendations.

[0059] Furthermore, thresholds are determined based on sensitive SOC ranges under charging conditions. This takes into account the characteristics exhibited by the battery under charging conditions, particularly the higher sensitivity to fluctuations in parameters such as battery voltage within certain SOC ranges. By setting independent thresholds for sensitive SOC ranges under charging conditions, the battery management system can more accurately reflect the battery's true state under these critical scenarios, enhancing its targeted and adaptable capabilities for detecting anomalies during the charging process.

[0060] As an optional embodiment, determining whether the vehicle battery pack is abnormal based on the voltage fluctuation value corresponding to the target SOC range under charging conditions includes: when the vehicle battery pack includes multiple battery modules, determining the voltage fluctuation value corresponding to the target SOC range under charging conditions for each battery module; and determining sub-abnormal results corresponding to each of the multiple battery modules based on the voltage fluctuation value corresponding to the target SOC range under charging conditions for each battery module.

[0061] In this embodiment, the detection of whether the vehicle battery pack is abnormal is described.

[0062] This involves battery modules, which are the basic building blocks of vehicle battery packs. They are typically composed of multiple individual battery cells connected in series or parallel and are the smallest physical objects for voltage monitoring and consistency assessment.

[0063] This includes sub-abnormal results, which are abnormal state conclusions determined for each individual battery module in the vehicle battery pack based on the voltage fluctuation value in the target SOC range under the charging conditions. These conclusions are used to indicate whether the specific module has performance abnormalities.

[0064] In this step, firstly, for each battery module within the battery pack, the voltage fluctuation value corresponding to each target SOC range under the stated charging condition is extracted. This step extends the overall analysis of the battery pack down to each specific component module, achieving finer-grained state monitoring. Then, based on the voltage fluctuation value of each battery module under the stated charging condition and each target SOC range, anomaly detection is performed independently, generating sub-anomaly results corresponding to that module. This allows anomaly detection to be precise down to the specific faulty or performance-degraded module.

[0065] By independently analyzing the behavior of each module during the critical charging phase (target SOC range), the specific module exhibiting performance deviations can be located earlier and more accurately, improving the precision of fault location. Based on the sub-anomaly results, more targeted maintenance strategies can be implemented, avoiding a single maintenance approach, improving maintenance efficiency and reducing costs. Simultaneously, timely identification and handling of anomalous modules helps prevent defects in a single module from affecting the performance and safety of the entire battery pack, thereby extending the overall lifespan of the battery pack.

[0066] As an optional embodiment, the abnormal result of whether the vehicle battery pack is abnormal is determined based on the voltage fluctuation value corresponding to the target SOC range under charging conditions. This includes: if the abnormal result includes a consistency abnormal result, determining the consistency index between multiple battery modules based on the voltage fluctuation value corresponding to the target SOC range under charging conditions for each battery module; and determining that the vehicle battery pack has a consistency abnormal result if the consistency index is lower than the consistency threshold.

[0067] In this embodiment, the steps for determining inconsistent results are described.

[0068] This includes a consistency index, which is used to quantify the performance consistency of individual battery modules within a battery pack under charging conditions and within a specific target SOC range. This index is calculated based on the voltage fluctuation values ​​of each module within this range. A high consistency index indicates small performance differences between modules, while a low consistency index indicates significant performance differences and inconsistencies between modules.

[0069] This involves a consistency threshold, which is a pre-set limit value used to determine whether the consistency index is at a normal level. If the calculated consistency index is lower than this threshold, the battery pack is determined to have an consistency anomaly.

[0070] This step involves calculating a quantitative indicator, or consistency index, representing overall consistency based on the voltage fluctuation values ​​of each battery module under the specified charging conditions and corresponding target SOC range. This calculation is performed using specific statistical algorithms (such as calculating the dispersion, range, or normalized distribution differences of voltage fluctuation values ​​between modules). This step aims to comprehensively evaluate the behavioral synchronicity of all modules during critical charging phases at the battery pack level. The calculated consistency index is then compared to a preset consistency threshold. If the index is below the threshold, it indicates that the performance differences between modules have exceeded acceptable limits, thus confirming an abnormal consistency result in the vehicle battery pack.

[0071] By calculating the consistency index and comparing it with a threshold, the existence of consistency issues in the battery pack can be objectively and quantitatively determined, avoiding errors from subjective judgment. Assessing consistency within sensitive SOC ranges under charging conditions allows for earlier warnings of consistency degradation trends caused by battery aging, imbalance, etc., providing timely and clear evidence for implementing intervention measures such as equalization maintenance. Anomaly detection based on the consistency index helps prevent a decrease in the usable capacity of the entire battery pack or the risk of overcharging or over-discharging due to the performance degradation of individual modules, thereby ensuring the safe operation of the battery pack and optimizing its overall performance and lifespan.

[0072] Furthermore, battery performance changes over time, so the target SOC range and battery characteristic parameters should be able to intelligently and dynamically adjust to adapt to the natural aging process of the battery. Machine learning algorithms, such as Support Vector Machines (SVM) or neural networks, can be used to perform deep learning on historical data, thereby automatically identifying and adjusting the target SOC range. In addition to voltage fluctuations, data such as temperature changes, current fluctuations, and changes in battery internal resistance can also be incorporated into the analysis of the target SOC range, forming a comprehensive evaluation model with multiple dimensions to gain a more complete understanding of the battery's state.

[0073] As an optional embodiment, based on the voltage fluctuation value of each battery module in the target SOC range under charging conditions, a consistency index among multiple battery modules is determined, including: for each battery module, based on the voltage fluctuation value in the target SOC range under charging conditions, determining the corresponding voltage fluctuation sum within a preset time period; based on the corresponding voltage fluctuation sum, determining the mean of the corresponding voltage fluctuation sum and the standard deviation of the corresponding voltage fluctuation sum; determining the difference between the corresponding voltage fluctuation sum and the corresponding mean; determining the ratio of the difference to the corresponding standard deviation; and determining the consistency index among multiple battery modules based on the ratios corresponding to each of the multiple battery modules.

[0074] In this embodiment, the steps for calculating the consistency index among multiple battery modules are described.

[0075] This includes the mean, which is the arithmetic mean of the voltage fluctuations of all battery modules, representing the central trend of the cumulative voltage fluctuation level of the battery pack under the stated charging conditions.

[0076] This involves the standard deviation, which is the standard deviation of the voltage fluctuations of all battery modules. It is used to quantify the dispersion of the voltage fluctuations of each module relative to the mean and to measure the statistical quantity of the cumulative fluctuation differences between modules.

[0077] In this step, firstly, for each battery module in the battery pack, a single cumulative value representing its overall fluctuation behavior, namely the sum of voltage fluctuations, is calculated based on its voltage fluctuation values ​​in each target SOC range under the stated charging conditions. Then, based on the sums of voltage fluctuations for all modules, their mean and standard deviation are calculated to understand the overall fluctuation level and the degree of difference between modules. Next, for each module, the difference between its sum of voltage fluctuations and the overall mean is calculated, and this difference is divided by the overall standard deviation to obtain the standardized proportion for each module. This standardization process ensures that the cumulative fluctuations of each module are compared on the same scale. Finally, based on the standardized proportions of all modules, a single consistency index is synthesized using specific statistical algorithms (such as calculating the variance, range, or constructing a distribution model of these proportions). This index comprehensively reflects the consistency or dispersion of the behavior of all modules during the critical charging stages.

[0078] By calculating voltage fluctuations and performing standardization, biases caused by individual module differences or different measurement scales are effectively eliminated, making the consistency assessment more objective and accurate. The consistency index, as a comprehensive indicator, can clearly quantify the overall consistency status of the battery pack, providing a clear numerical basis for anomaly detection. This method enhances the reliability and repeatability of the assessment results, helps to detect consistency degradation trends between modules at an early stage, and thus supports more precise battery management and maintenance decisions.

[0079] As an optional embodiment, the abnormal result of whether the vehicle battery pack is abnormal is determined based on the voltage fluctuation value corresponding to the target SOC range under the charging condition. This includes: determining the voltage correlation coefficient corresponding to the charging condition when the abnormal result includes abnormal operation; determining the operating condition voltage difference index corresponding to the vehicle battery pack based on the voltage fluctuation value corresponding to the target SOC range under the charging condition and the voltage correlation coefficient; and determining that the vehicle battery pack has an abnormal operating condition when the operating condition voltage difference index is greater than a predetermined difference threshold.

[0080] This embodiment describes the steps for determining abnormal operating conditions.

[0081] This includes the operating condition voltage difference index, a comprehensive indicator calculated based on voltage fluctuation values ​​within each target SOC range under the stated charging conditions, combined with a voltage correlation coefficient. This index quantifies the overall deviation between the observed voltage fluctuation pattern and the expected or historical normal pattern, comprehensively assessing the operational status of the charging process.

[0082] This involves a predetermined difference threshold, which is a pre-set critical value used to determine whether the operating voltage difference index indicates an anomaly. If the calculated operating voltage difference index is greater than this threshold, it is determined that there is an operational anomaly in the charging process.

[0083] For each battery module, firstly, based on historical normal data or battery models, the inherent correlation characteristics between voltage fluctuation values ​​in each target SOC range under the stated charging condition are analyzed and calculated to determine one or more voltage correlation coefficients to characterize the normal charging fluctuation pattern. Then, the actual voltage fluctuation values ​​collected during the current charging process in each target SOC range under the stated charging condition are combined with the voltage correlation coefficients, and a comprehensive operating condition voltage difference index is calculated using a specific mathematical model or statistical algorithm. This index reflects the overall difference between the current charging fluctuation pattern and the normal pattern. Finally, the calculated operating condition voltage difference index is compared with a preset predetermined difference threshold. If the index is greater than the threshold, it indicates that the voltage fluctuation behavior of the current charging process has significantly deviated from the normal pattern, thus determining that the on-board battery pack has an abnormal operating condition.

[0084] By analyzing the correlation patterns of voltage fluctuations during critical charging stages and calculating the difference index, non-local, non-incidental systemic operational deviations can be detected, such as anomalies caused by factors like unstable charger output, global temperature gradients, or communication interference. This pattern-difference-based detection method enhances the depth of monitoring the overall operational status of the charging process, improves the early warning capability for potential systemic risks, and helps ensure charging safety and battery health at the process level.

[0085] As an optional embodiment, determining the voltage fluctuation value of the corresponding target SOC range based on the voltage fluctuation value between corresponding adjacent data frames includes: obtaining the battery characteristic parameters of the vehicle battery pack; determining the target SOC range corresponding to each operating condition based on the battery characteristic parameters, wherein the corresponding target SOC range is the state range where the probability of an anomaly is greater than a predetermined probability under the corresponding operating condition; and determining the voltage fluctuation value of the corresponding target SOC range based on the voltage fluctuation value between corresponding adjacent data frames.

[0086] In this embodiment, the steps of determining the target SOC range corresponding to each operating condition and obtaining its voltage fluctuation value are described.

[0087] This involves battery characteristic parameters, including battery type, chemical system, rated capacity, operating voltage range, temperature characteristics, charge and discharge efficiency, etc. These parameters are inherent physical and chemical properties of the battery that reflect its basic performance and behavioral characteristics.

[0088] This involves a predetermined anomaly probability, which is a pre-set statistical probability threshold used to quantify the likelihood of abnormal fluctuations in parameters such as battery voltage within a certain SOC range. If the historical statistical probability of an anomaly occurring within a certain SOC range is higher than this threshold, then that range is defined as the target SOC range.

[0089] In this step, firstly, the battery characteristic parameters of the vehicle battery pack are acquired. These parameters provide a foundation for understanding the expected behavior of the battery under different states of charge (SOC). Next, based on these battery characteristic parameters and combined with statistical analysis of a large amount of historical operating data, the SOC intervals most likely to expose performance differences between battery modules or their own anomalies under various operating conditions such as charging and discharging are identified and determined; these are the target SOC intervals. Finally, the voltage fluctuation values ​​between adjacent data frames are calculated, and all fluctuation values ​​falling within the determined target SOC intervals are selected. This yields a set of voltage fluctuation values ​​for each target SOC interval, providing focused core data for subsequent consistency analysis and anomaly detection.

[0090] By focusing voltage fluctuation analysis on the critical SOC ranges most likely to reveal problems, anomaly detection becomes more targeted, enabling earlier and more accurate detection of subtle early signs of battery performance degradation. The determination of target SOC ranges is closely integrated with the battery's own characteristics and historical operating patterns, effectively reducing the risk of false alarms from analyzing non-sensitive SOC range data, thereby improving the overall reliability of anomaly detection. The identified target SOC ranges provide crucial information for the battery management system to implement differentiated monitoring and refined management. When the battery operates within these ranges, more intensive sampling or preventative equalization can be triggered, helping to optimize battery life and ensure system safety.

[0091] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0092] In related technologies, there is a problem with the inaccuracy in determining whether an on-board battery pack is abnormal, especially in terms of consistency. This is explained below: In a battery pack composed of multiple individual cells connected in series or parallel, the performance differences between cells (such as capacity, internal resistance, self-discharge rate, and aging level) significantly affect the overall system efficiency and reliability. This inconsistency can lead to a decrease in the usable capacity of the battery pack, reduced energy utilization, and even localized overcharging or over-discharging, thereby accelerating battery aging or causing safety hazards. Therefore, consistency estimation has become one of the core functions of a battery management system (BMS), and its importance is mainly reflected in the following aspects: improving the energy utilization rate of the battery pack. The actual usable capacity of a battery pack is often limited by the worst-performing individual cell. Through consistency estimation, the BMS can dynamically adjust the charging and discharging strategy, reducing the "weakest link" effect and maximizing the energy output of the battery pack; ensuring system safety. Severe inconsistency may lead to the risk of thermal runaway. Consistency estimation can help the BMS provide early warnings of abnormal states and prevent safety accidents by cutting off circuits or triggering equalization mechanisms.

[0093] The SOC-OCV (State of Charge-Open Circuit Voltage) method indirectly estimates the SOC differences of individual cells by measuring the open circuit voltage (OCV) of the battery after it has been left to rest, combined with a pre-set SOC-OCV curve, thereby assessing the consistency of the battery pack. Its core assumption is that batteries with the same chemical system have similar OCVs at the same SOC. However, this method is highly dependent on static conditions, requires long periods of rest, and has poor time-sensitivity.

[0094] Through experiments, the SOC-OCV curve of lithium iron phosphate batteries can be obtained (the position of the curve will change depending on the cell material, but the basic shape remains the same). Figure 2 This is a schematic diagram of the SOC-OCV curve in the battery pack consistency estimation method in related technologies, such as... Figure 2 As shown, the curve exhibits two distinct plateaus. Within these plateau regions, as the State of Charge (SOC) increases, the OCV remains almost unchanged, rendering the SOC-OCV method ineffective. The SOC-OCV method is only feasible within the approximately 0-25% SOC range. Due to these two limitations, the application scope of this method for estimating the consistency of automotive lithium iron phosphate battery packs is very limited. It is evident that related technologies suffer from inaccurate determination of whether automotive battery packs are abnormal, particularly regarding consistency anomalies.

[0095] Based on the above, an optional embodiment of the present invention provides a method for determining abnormal results of an on-board battery pack. Specifically, it can be called a method for estimating the consistency of an on-board lithium iron phosphate battery pack. This method involves acquiring and processing the vehicle's operational monitoring data for the on-board lithium iron phosphate battery pack; classifying the vehicle's operating conditions to obtain charging data that meets the conditions; processing the charging data that meets the conditions to obtain the outlier degree of voltage fluctuation values ​​in different ranges of each module in the battery pack; and finally, based on the outlier degree index, obtaining the consistency estimate of the on-board lithium iron phosphate battery pack for new energy vehicles. This enhances battery safety and operational reliability, optimizes battery life and performance, improves user experience, and accurately determines whether the battery pack has any abnormalities, especially consistency anomalies.

[0096] Figure 3 This is a schematic diagram of the charging curves of normal and abnormal modules in a consistency estimation method for vehicle-mounted lithium iron phosphate battery packs provided by an optional embodiment of the present invention. Figure 4 This is a schematic diagram illustrating the distribution of voltage fluctuation anomalies in normal and abnormal modules under different SOC states in a consistency estimation method for vehicle-mounted lithium iron phosphate battery packs provided by an optional embodiment of the present invention. Figure 3 , 4 As shown, it will be introduced below:

[0097] S1: Obtain vehicle operation monitoring data of the on-board lithium iron phosphate battery pack (same as above for obtaining the operation data of the on-board battery pack, which includes multiple battery modules).

[0098] Furthermore, the data obtained in step S1 includes vehicle identification code, acquisition time, SOC, current, and individual cell voltage. It is understood that this data can be obtained through historical data collection, database queries, or vehicle-to-everything (V2X) networks.

[0099] S2: Data preprocessing, including cleaning, sorting and other operations.

[0100] In step S2, the data obtained in step S1 is cleaned: invalid, out-of-validity range, and duplicate data are removed. These abnormal data can greatly interfere with the calculation of voltage fluctuation values. For example, values ​​with an SOC range not within 0-100 or individual cell voltage range not within 0-10V are considered interference data that are out of the valid range.

[0101] Furthermore, the cleaned data is sorted in ascending order by vehicle identification code and time, and the time difference between adjacent data frames is obtained to facilitate subsequent estimation steps. Understandably, since anomalies may occur in the time difference between the first and last lines, the time difference data between the first and last lines is appropriately filled in.

[0102] Furthermore, during transmission, vehicle operation data may be interrupted due to network signal fluctuations (such as 4G / 5G coverage blind spots or signal attenuation) or communication delays, resulting in discontinuous real-time vehicle data. To ensure the completeness of subsequent analysis, this portion of the data needs to be appropriately supplemented.

[0103] S3: Divide the operating conditions to obtain the vehicle charging, discharging and resting intervals. In the optional embodiments of the invention, the charging data can be mainly selected as the main research object (similar to the above-mentioned division of the operating data into operating conditions to obtain the sub-operating data corresponding to the charging conditions).

[0104] In step S3, the data processed in step S2 is combined with vehicle status field data such as charging status, vehicle status, current, and SOC to divide the vehicle data's operating condition range into a charging range, a discharging range, and a resting range. It is understood that the resting range calculation only begins after the vehicle has been stationary for a specific threshold time.

[0105] Furthermore, charging state data that meet the time interval, vehicle status, and current threshold are selected as the main research object.

[0106] S4: For a certain segment of charging data that meets the conditions, group it according to different SOC states to obtain the module voltage fluctuation value between adjacent data frames (similar to the above, based on the corresponding sub-operation data under the charging condition, the voltage fluctuation value between adjacent data frames under the charging condition is determined, where adjacent data frames refer to data frames corresponding to two consecutive sampling times arranged in chronological order).

[0107] Step S4 includes obtaining the module voltage fluctuation value between adjacent data frames for a certain segment of charging data.

[0108] Furthermore, the data is grouped and processed according to the SOC status;

[0109] The voltage fluctuation values ​​of each module are summed according to different groupings. Table 1 is an example table of voltage fluctuation values ​​of each module under a certain SOC state provided by an optional embodiment of the present invention, as shown in Table 1:

[0110] Table 1

[0111]

[0112] S5: Based on the above data, sum and normalize the module voltage fluctuation values ​​under different SOC states in all charging intervals within the vehicle's preset interval (similar to the above for charging conditions, determining the voltage fluctuation value of the corresponding target SOC interval based on the voltage fluctuation values ​​between adjacent data frames).

[0113] In step S5, after processing the data according to the above steps, the voltage fluctuation values ​​of each module under different SOC states of a certain segment of vehicle charging data are obtained.

[0114] Understandably, in the consistency estimation of on-board lithium iron phosphate battery packs based on charging data, the measurement data is susceptible to interference from various factors (such as sensor noise, temperature fluctuations, and unstable charger output), leading to abnormal fluctuations in the estimation results. To improve the reliability of the evaluation, the original estimation data is expanded from a single charging interval to all charging process data within a preset time period;

[0115] Furthermore, all charging data within a preset time period are aggregated and processed to obtain the sum of voltage fluctuation values ​​of each module in a certain lithium iron phosphate battery pack under various SOC states within a preset period. Table 2 is an example table of voltage fluctuation values ​​of each module under a certain SOC state provided by an optional embodiment of the present invention, as shown in Table 2:

[0116] Table 2

[0117]

[0118] Due to individual differences in user usage patterns and charging behaviors, there is heterogeneity in the distribution of State of Charge (SOC) across different vehicles (including initial SOC and final SOC), and the same vehicle also exhibits significant differences in state distribution across different charging cycles.

[0119] Therefore, the voltage fluctuations of each module under different SOC states are not directly comparable. The voltage fluctuations of each module under a specific SOC state of the vehicle are normalized as follows: (ΔV) i -Avg(ΔV)) / Std(ΔV). Where ΔV i Let represent the sum of voltage fluctuations of the i-th module, Avg(ΔV) represent the mean of the sum of voltage fluctuations of all modules, and Std(ΔV) represent the standard deviation of the sum of voltage fluctuations of all modules.

[0120] Table 3 is an example table of the results after normalizing the fluctuation values ​​of each module voltage under a certain SOC state provided by the optional embodiment of the present invention, as shown in Table 3:

[0121] Table 3

[0122]

[0123] Furthermore, the normalized data represents the degree of voltage fluctuation deviation.

[0124] S6: Determine whether there is an abnormality in the consistency of the vehicle-mounted lithium iron phosphate battery pack based on the threshold (similar to the above, based on the voltage fluctuation value of each battery module under the charging condition corresponding to the target SOC range, determine the consistency index between multiple battery modules; if the consistency index is lower than the consistency threshold, it is determined that there is an abnormal consistency result in the vehicle-mounted battery pack).

[0125] In step S6, based on the above data, the data is reorganized by module to obtain the voltage fluctuation deviation of different modules under various SOC states, as well as the range and standard deviation of the voltage fluctuation deviation of different modules under all SOC states. Table 4 is an example table of results provided by the optional implementation of the present invention, as shown in Table 4:

[0126] Table 4

[0127]

[0128] Furthermore, the maximum value of the range and standard deviation of the voltage fluctuation deviation of each module is the criterion for determining the consistency anomaly of a vehicle. When both exceed the set threshold, the consistency of the vehicle's lithium iron phosphate battery pack is determined to be abnormal.

[0129] S7: If there is an abnormality in the consistency of the vehicle-mounted lithium iron phosphate battery pack, calculate the consistency magnitude and identify the faulty module.

[0130] Understandably, when there is an abnormality in the consistency of the vehicle-mounted lithium iron phosphate battery pack, the abnormal module is the module whose voltage fluctuation deviation range and standard deviation are both greater than the set threshold.

[0131] The consistency difference of the abnormal module is the difference between the SOC state with the largest voltage fluctuation deviation and the SOC state with the smallest voltage fluctuation deviation.

[0132] The above optional implementation methods can achieve at least the following beneficial effects: a wider range of application scenarios: no need to operate below 25% SOC, no resting requirement. It only requires the battery to reach approximately 65% ​​SOC during a stable charging process, which not only solves the inaccuracy problem in determining whether the vehicle battery pack is abnormal in related technologies, but also solves the problem that some vehicles cannot be evaluated due to not meeting the consistency evaluation conditions.

[0133] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0134] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to 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 the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0135] Example 2

[0136] According to embodiments of the present invention, an apparatus for implementing the above-described method for determining anomalies in a battery pack is also provided. Figure 5 This is a structural block diagram of a battery pack anomaly determination device according to an embodiment of the present invention, such as... Figure 5 As shown, the device includes: an acquisition module 502, a first determination module 504, a second determination module 506, a third determination module 508, and a fourth determination module 510. The device will be described in detail below.

[0137] The module 502 is used to acquire the operating data of the vehicle battery pack; the first determining module 504, connected to the acquisition module 502, is used to divide the operating data into operating conditions to obtain the corresponding operating condition data under the charging condition; the second determining module 506, connected to the first determining module 504, is used to determine the voltage fluctuation value between adjacent data frames under the charging condition based on the operating condition data, wherein adjacent data frames refer to data frames corresponding to two consecutive sampling times arranged in chronological order; the third determining module 508, connected to the second determining module 506, is used to determine the voltage fluctuation value of the corresponding target state of charge (SOC) range based on the voltage fluctuation value between corresponding adjacent data frames; the fourth determining module 510, connected to the third determining module 508, is used to determine whether the vehicle battery pack is abnormal based on the voltage fluctuation value of the corresponding target SOC range under the charging condition.

[0138] It should be noted here that the above-mentioned acquisition module 502, first determination module 504, second determination module 506, third determination module 508 and fourth determination module 510 correspond to steps S102 to S110 in the method for determining the anomaly of the battery pack. The multiple modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0139] Example 3

[0140] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the battery pack anomaly determination method of any of the above embodiments.

[0141] Example 4

[0142] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the battery pack anomaly determination method described above.

[0143] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0144] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0146] The units described as separate components may or may not be physically separate. 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0148] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0149] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining anomalies in a battery pack, characterized in that, include: Acquire operational data of the vehicle's battery pack; The operating data is divided into operating conditions to obtain the corresponding operating condition data under the charging condition. Based on the operating data, the voltage fluctuation value between adjacent data frames under the charging condition is determined, wherein the adjacent data frames refer to the data frames corresponding to two consecutive sampling times arranged in chronological order; Based on the voltage fluctuation values ​​between adjacent data frames, determine the voltage fluctuation values ​​for the corresponding target state of charge (SOC) range. Based on the voltage fluctuation value corresponding to the target SOC range under the charging condition, an abnormal result is determined as to whether the vehicle battery pack is abnormal.

2. The method according to claim 1, characterized in that, Based on the voltage fluctuation value corresponding to the target SOC range under the stated charging condition, the abnormal result of determining whether the vehicle battery pack is abnormal includes: Based on the voltage fluctuation values ​​of the corresponding target SOC range, an anomaly index is determined for each target SOC range, wherein the anomaly index includes at least one of the following: range, standard deviation; Determine the abnormal thresholds corresponding to the target SOC range under the aforementioned charging conditions; The abnormal result is determined based on the abnormal index and abnormal threshold corresponding to the target SOC range under the charging condition.

3. The method according to claim 1, characterized in that, Based on the voltage fluctuation value corresponding to the target SOC range under the stated charging condition, the abnormal result of determining whether the vehicle battery pack is abnormal includes: When the vehicle battery pack includes multiple battery modules, for each battery module, determine the voltage fluctuation value corresponding to the target SOC range under the charging condition; Based on the voltage fluctuation value of each battery module under the charging condition corresponding to the target SOC range, sub-abnormal results corresponding to each of the multiple battery modules are determined.

4. The method according to claim 3, characterized in that, Based on the voltage fluctuation value corresponding to the target SOC range under the stated charging condition, the abnormal result of determining whether the vehicle battery pack is abnormal includes: In the case where the abnormal results include consistency abnormal results, based on the voltage fluctuation value of each battery module in the target SOC range under the charging condition, the consistency index among the multiple battery modules is determined. If the consistency index is lower than the consistency threshold, it is determined that the vehicle battery pack has an abnormal consistency result.

5. The method according to claim 4, characterized in that, Based on the voltage fluctuation value of each battery module under the charging condition corresponding to the target SOC range, a consistency index among the multiple battery modules is determined, including: For each battery module, based on the voltage fluctuation value of the target SOC range under the charging condition, the corresponding voltage fluctuation within a preset time period is determined; Based on the corresponding voltage fluctuations, determine the mean of the corresponding voltage fluctuations and the standard deviation of the corresponding voltage fluctuations; Determine the corresponding voltage fluctuation and its difference from the corresponding mean; Determine the ratio of the difference to the corresponding standard deviation; The consistency index among the multiple battery modules is determined based on their respective proportions.

6. The method according to claim 1, characterized in that, Based on the voltage fluctuation value corresponding to the target SOC range under the stated charging condition, the abnormal result of determining whether the vehicle battery pack is abnormal includes: In the case where the abnormal results include abnormal operating conditions, the voltage correlation coefficient corresponding to the charging condition is determined. Based on the voltage fluctuation value of the target SOC range under the charging condition and the voltage correlation coefficient, the operating condition voltage difference index corresponding to the vehicle battery pack is determined. If the voltage difference index under the operating condition is greater than a predetermined difference threshold, it is determined that the vehicle battery pack has an abnormal operating condition.

7. The method according to any one of claims 1 to 6, characterized in that, Based on the voltage fluctuation values ​​between adjacent data frames, determine the voltage fluctuation values ​​for the corresponding target SOC range, including: Obtain the battery characteristic parameters of the vehicle battery pack; Based on the battery characteristic parameters, the target SOC range corresponding to each operating condition is determined, wherein the corresponding target SOC range is the state range in which the probability of an anomaly is greater than the predetermined probability of an anomaly under the corresponding operating condition. Based on the voltage fluctuation values ​​between adjacent data frames, determine the voltage fluctuation value of the corresponding target SOC range.

8. A device for determining anomalies in a battery pack, characterized in that, include: The acquisition module is used to acquire the operating data of the vehicle battery pack; The first determining module is used to divide the operating data into operating conditions to obtain the corresponding operating condition operating data under the charging operating condition. The second determining module is used to determine the voltage fluctuation value between adjacent data frames under the charging condition based on the operating data, wherein the adjacent data frames represent data frames corresponding to two consecutive sampling times arranged in chronological order. The third determining module is used to determine the voltage fluctuation value of the corresponding target state of charge (SOC) range based on the voltage fluctuation value between corresponding adjacent data frames. The fourth determining module is used to determine whether the on-board battery pack is abnormal based on the voltage fluctuation value corresponding to the target SOC range under the charging condition.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the battery pack anomaly determination method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the battery pack anomaly determination method as described in any one of claims 1 to 7.

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