Methods and apparatus for evaluating battery pack conformity, storage media, electronic devices

Abstract

This application discloses a method and apparatus, storage medium, electronic device, and computer program product for evaluating battery pack consistency. The method includes: acquiring cell data of multiple cells of the battery pack to be evaluated under target operating conditions; determining a dispersion index of the battery pack based on the standard deviation and average value of the cell data; determining a first evaluation index of the battery pack based on the dispersion index; uploading the cell data of the multiple cells to the cloud when the first evaluation index is determined to be greater than a preset threshold, and receiving a second evaluation index of the battery pack returned from the cloud; and determining the evaluation result of battery pack consistency under the target operating conditions based on the first and second evaluation indices. By fully leveraging the real-time advantages of vehicle-side computing and the accuracy advantages of cloud computing, the real-time performance and accuracy of battery pack consistency evaluation are ensured, enabling timely detection of battery pack consistency issues.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and more specifically, to a method and apparatus for evaluating battery pack consistency, a storage medium, an electronic device, and a computer program product. Background Technology

[0002] With the rapid development of the new energy vehicle industry, people are paying increasing attention to the safety and reliability of new energy vehicles. As a core component of new energy vehicles, the battery pack's safety and lifespan directly affect the vehicle's performance and reliability. NCM battery packs (nickel-manganese-cobalt battery packs) feature high energy density, high power density, long lifespan, and a wide operating temperature range, leading to their increasingly widespread application in new energy vehicles. However, NCM battery packs can experience dispersion during use. Dispersion refers to significant differences in the performance, state, or parameters of the individual cells within the battery pack, resulting in uneven charge distribution, prolonged charging time, shortened lifespan, and performance degradation. This phenomenon reduces battery performance and lifespan, consequently impacting vehicle performance and safety.

[0003] In related technologies, battery pack data is typically acquired during vehicle maintenance, and then the consistency of the battery pack is determined through cloud-based data analysis.

[0004] However, the assessment methods in the relevant technologies are not timely enough and the assessment results are not accurate enough because the assessment is only carried out during maintenance and the vehicle is in a stationary condition at the time of assessment. Summary of the Invention

[0005] This application provides a method and apparatus for evaluating battery pack consistency, a storage medium, an electronic device, and a computer program product.

[0006] According to one aspect of the embodiments of this application, a method for evaluating battery pack consistency is provided. The method includes: acquiring cell data of multiple cells of a battery pack to be evaluated under a target operating condition; determining a dispersion index of the battery pack based on the standard deviation and average value of the cell data of the multiple cells; determining a first evaluation index of the battery pack based on the dispersion index of the battery pack; uploading the cell data of the multiple cells to the cloud when the first evaluation index is determined to be greater than a preset threshold, and receiving a second evaluation index of the battery pack returned by the cloud; and determining an evaluation result of battery pack consistency under the target operating condition based on the first evaluation index and the second evaluation index.

[0007] In an exemplary embodiment, the dispersion coefficient of each time point in the current preset time period is determined based on the standard deviation and average value of the cell data of multiple cells at each time point in the current preset time period; the first dispersion coefficient of the battery pack to be evaluated is determined based on the average value of the dispersion coefficients of each time point in the current preset time period; and the first dispersion index of the battery pack is determined based on the first dispersion coefficient and the corresponding first threshold.

[0008] In an exemplary embodiment, the rate of change of the coefficient of variation of the battery pack to be evaluated is determined based on the first coefficient of variation and the second coefficient of variation of the battery pack to be evaluated in the previous preset time period; and the second dispersion index of the battery pack is determined based on the rate of change of the coefficient of variation and the corresponding second threshold.

[0009] In an exemplary embodiment, a correlation coefficient between the discrete coefficient and the cell voltage difference is determined based on the discrete coefficient at each time point within the current preset time period, the average of the discrete coefficients within the current preset time period, the cell voltage difference at each time point within the current preset time period, and the average of the cell voltage difference within the current preset time period; and a third discreteness index of the battery pack is determined based on the correlation coefficient and the corresponding third threshold.

[0010] In one exemplary embodiment, a first evaluation index for the battery pack is determined based on a first dispersion index, a second dispersion index, and a third dispersion index.

[0011] In an exemplary embodiment, the method further includes: determining the evaluation results of the battery pack consistency under multiple different operating conditions; and determining the comprehensive evaluation result of the battery pack consistency under multiple different operating conditions based on the evaluation results of the battery pack consistency under multiple different operating conditions.

[0012] In an exemplary embodiment, the method further includes: adjusting the battery balancing strategy of the battery pack to improve the consistency of the battery pack when the comprehensive evaluation result is less than or equal to the maintenance threshold; and issuing a maintenance reminder signal when the comprehensive evaluation result is greater than the maintenance threshold.

[0013] Another aspect of this application provides a battery pack consistency evaluation device, comprising: a data acquisition module for acquiring cell data of multiple cells of a battery pack to be evaluated under target operating conditions; a dispersion determination module for determining a dispersion index of the battery pack based on the standard deviation and average value of the cell data of the multiple cells; a first index determination module for determining a first evaluation index of the battery pack based on the dispersion index of the battery pack; a second index determination module for uploading the cell data of the multiple cells to the cloud and receiving the second evaluation index of the battery pack returned by the cloud when the first evaluation index is determined to be greater than a preset threshold; and an evaluation module for determining the evaluation result of battery pack consistency under target operating conditions based on the first evaluation index and the second evaluation index.

[0014] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to execute the above-described battery pack consistency evaluation method at runtime.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described battery pack consistency evaluation method through the computer program.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the methods described in various embodiments of this application.

[0017] The aforementioned battery pack consistency evaluation method, on the one hand, enables real-time monitoring and analysis of battery pack cell data at the vehicle end, allowing for real-time assessment of the battery pack's consistency status and timely detection of potential inconsistencies, thus improving the real-time performance of the battery pack consistency evaluation. On the other hand, for battery packs with poor consistency, cloud-based big data analysis can obtain more comprehensive evaluation indicators, improving the accuracy and reliability of the evaluation. The evaluation result of the battery pack is determined by combining the first and second evaluation indicators assessed by the vehicle end and the cloud end, respectively. By closely integrating cloud and vehicle-side data, the advantages of real-time vehicle-side computing and the accuracy of cloud computing are fully utilized. The combination of real-time vehicle-side computing and in-depth cloud analysis ensures the real-time and accurate nature of the battery pack consistency evaluation, enabling timely detection and early warning of battery pack consistency issues. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of 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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a hardware structure block diagram of the vehicle terminal of the battery pack consistency evaluation method according to an embodiment of this application;

[0021] Figure 2 This is a flowchart of a battery pack consistency evaluation method according to an embodiment of this application;

[0022] Figure 3 This is a second flowchart of a battery pack consistency evaluation method according to an embodiment of this application;

[0023] Figure 4 This is the third flowchart of a battery pack consistency evaluation method according to an embodiment of this application;

[0024] Figure 5 This is the fourth flowchart of a battery pack consistency evaluation method according to an embodiment of this application;

[0025] Figure 6 This is the fifth flowchart of a battery pack consistency evaluation method according to an embodiment of this application;

[0026] Figure 7 This is the sixth flowchart of a battery pack consistency evaluation method according to an embodiment of this application;

[0027] Figure 8 This is a block diagram of a battery pack consistency evaluation method according to an embodiment of this application;

[0028] Figure 9 This is a structural block diagram of an optional battery pack consistency evaluation device according to an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 used in this way can be interchanged where appropriate so that the embodiments of this application 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 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.

[0031] The methods and embodiments provided in this application can be executed in an in-vehicle terminal or a similar computing device. Taking running on an in-vehicle terminal as an example, Figure 1 This is a hardware structure block diagram of a computer terminal for the battery pack consistency evaluation method according to an embodiment of this application. Figure 1 As shown, a computer terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor (MPU) or a programmable logic device (PLD)) and a memory 104 for storing data are also shown. In one exemplary embodiment, the vehicle-mounted terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned vehicle-mounted terminal. For example, the vehicle-mounted terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 Equivalent functions or ratios shown Figure 1 The functions shown have more different configurations.

[0032] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the battery pack consistency evaluation method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the vehicle terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0033] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the vehicle terminal. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0034] This embodiment provides a method for evaluating battery pack consistency, applied to an in-vehicle terminal. Figure 2 This is a flowchart of an optional battery pack consistency evaluation method according to an embodiment of this application, the process including the following steps S200-S240:

[0035] Step S200: Obtain cell data of multiple cells of the battery pack to be evaluated under the target operating conditions.

[0036] The target operating condition can include either a static operating condition or a dynamic operating condition. The dynamic operating condition can include fast charging, slow charging, driving, and other operating conditions.

[0037] Specifically, this can involve continuously acquiring cell data from multiple cells within the battery pack under target operating conditions, recording the cell data at each point in time for subsequent processing and calculation. Cell data can include key parameters such as voltage, current, and temperature.

[0038] Step S210: Determine the dispersion index of the battery pack based on the standard deviation and average value of the cell data of multiple cells.

[0039] This process involves using cell data to calculate its standard deviation and mean, thereby determining the dispersion index of the battery pack. The dispersion index reflects the consistency of parameters among the cells. It can be represented by the coefficient of variation, which is calculated as: coefficient of variation = standard deviation of cell data / mean of cell data.

[0040] Step S220: Determine the first evaluation index of the battery pack based on the dispersion index of the battery pack.

[0041] Specifically, based on the dispersion index, a first evaluation index for the battery pack can be determined. This index is used to initially determine the consistency status of the battery pack, and the first evaluation index can be directly calculated at the vehicle end, resulting in better real-time performance in determining the consistency status of the battery pack.

[0042] Step S230: If the first evaluation index is determined to be greater than the preset threshold, the cell data of multiple cells are uploaded to the cloud, and the second evaluation index of the battery pack is received from the cloud.

[0043] When the first evaluation indicator exceeds the preset threshold, it indicates that there may be a problem with the consistency of the battery pack. At this time, the cell data is uploaded to the cloud for more in-depth analysis. The cloud calculates the second evaluation indicator based on advanced algorithms and big data models.

[0044] For example, the fuzzy entropy of the battery pack can be calculated in the cloud. The formula for calculating the fuzzy entropy is as follows.

[0045]

[0046] FuzzyEn(t)=lnθ m (t)-lnθ m+1 (t)

[0047] Where m is the time window, dividing the time series into n-m+1 sequences, i and j are two consecutive time points in the sequence, and d ij Let r be the distance between sequences, and r be the mean. Let θ be the fuzzy membership degree, θ be the average membership degree, and FuzzyEn(t) be the fuzzy entropy value.

[0048] By utilizing the fuzzy entropy algorithm, the regularity and predictability of battery voltage sequence changes over time can be captured, helping to identify inconsistencies and potential fault risks. If the calculated fuzzy entropy value is higher than a set threshold, it indicates high complexity of the voltage sequence and potential inconsistencies, requiring further early warning, alarm, and optimization measures. Furthermore, by comparing the fuzzy entropy values ​​within different time windows, the trend of battery pack consistency changes over time can be monitored, which is crucial for early detection and handling of problems, and for improving the consistency, stability, and safety of the battery pack.

[0049] By comparing the calculated fuzzy entropy with the set threshold, a second evaluation index can be obtained. For example, if the fuzzy entropy of the vehicle battery pack is calculated to be 1.3687 within the monthly time window, which is greater than the set threshold of 1.1, then the second evaluation index is considered to be = fuzzy entropy / threshold = 0.24.

[0050] Step S240: Determine the evaluation result of battery pack consistency under the target operating condition based on the first evaluation index and the second evaluation index.

[0051] Specifically, by comprehensively considering both the first and second evaluation metrics, the final evaluation result for battery pack consistency under the target operating conditions is determined. The vehicle receives the second evaluation metric returned from the cloud and combines it with the first evaluation metric, using a predefined evaluation algorithm (e.g., weighted average) to determine the final battery pack consistency evaluation result. The evaluation result will be used for subsequent battery status monitoring, early warning, and optimization strategy development.

[0052] In this embodiment, on the one hand, by monitoring and analyzing the cell data of the battery pack in real time at the vehicle end, the consistency status of the battery pack can be assessed in real time, and potential inconsistencies can be detected in a timely manner, improving the real-time performance of the battery pack consistency assessment. On the other hand, for battery packs with poor consistency, cloud-based big data analysis can be used to obtain more comprehensive evaluation indicators, improving the accuracy and reliability of the assessment. The evaluation result of the battery pack is determined by combining the first and second evaluation indicators assessed by the vehicle end and the cloud end respectively. By closely integrating cloud and vehicle-side data, the advantages of real-time vehicle-side computing and accuracy cloud computing are fully utilized. The combination of real-time vehicle-side computing and in-depth cloud analysis ensures the real-time performance and accuracy of the battery pack consistency assessment, enabling timely detection and early warning of battery pack consistency issues.

[0053] In one embodiment, such as Figure 3 As shown, step S210 determines the dispersion index of the battery pack based on the standard deviation and average value of the cell data from multiple cells. This includes steps S300-S320. Wherein:

[0054] Step S300: Determine the coefficient of variation for each time point in the current preset time period based on the standard deviation and average value of the cell data of multiple cells at each time point in the current preset time period.

[0055] The preset time period can be one day, one week, or one month. Specifically, multiple time points (such as every minute or every hour) can be selected, and the standard deviation and average value of the cell data for multiple cells at multiple time points can be calculated. For the data at each time point, the standard deviation (reflecting the degree of fluctuation of the cell voltage) and the average value (reflecting the central trend of the cell voltage) are calculated. Then, the coefficient of variation is calculated, i.e., (coefficient of variation = standard deviation / average value). This indicator reflects the consistency of the cell voltage at that time point.

[0056] Step S310: Determine the first discrete coefficient of the battery pack to be evaluated based on the average value of the discrete coefficients at each time point within the current preset time period.

[0057] Within a preset time period, the dispersion coefficients at each time point are averaged to obtain the first dispersion coefficient of the battery pack for the current time period. This step integrates the cell voltage consistency status throughout the entire time period.

[0058] Step S320: Determine the first dispersion index of the battery pack based on the first dispersion coefficient and the corresponding first threshold.

[0059] The process involves comparing a first coefficient of variation with a preset first threshold. If the first coefficient of variation is higher than the first threshold, it indicates that the consistency of the battery pack is lower than expected; conversely, it indicates good consistency. A first degree of variation index can be calculated. For example, if the coefficient of variation for the battery pack this month is 0.0012, which is greater than the threshold of 0.001, then the consistency index A2 is considered to be: coefficient of variation / threshold - 1 = 0.2.

[0060] In this embodiment, by calculating the dispersion coefficient at each time point, the vehicle-side system can monitor the consistency status of the battery pack cells in real time, promptly detect potential inconsistencies, achieve early warning, and prevent a sharp decline in battery pack performance or safety accidents. When calculating the first dispersion coefficient, not only the instantaneous state is considered, but also the average value over a time period is used for comprehensive evaluation, ensuring the comprehensiveness and effectiveness of the evaluation and avoiding misjudgments caused by random factors. The setting of the first threshold can be optimized according to the characteristics and operating conditions of the battery pack to ensure the accuracy and rationality of the evaluation.

[0061] In one embodiment, such as Figure 4 As shown, step S210, determining the dispersion index of the battery pack based on the standard deviation and average value of the cell data of multiple cells, further includes steps S400-S410. Wherein:

[0062] Step S400: Determine the rate of change of the coefficient of variation of the battery pack to be evaluated based on the first coefficient of variation and the second coefficient of variation of the battery pack to be evaluated in the previous preset time period.

[0063] Specifically, at the end of the current preset time period, the BMS calculates the first coefficient of variation for that time period. It then reviews the data from the previous preset time period, retrieves and stores the second coefficient of variation at that time. Finally, it calculates the rate of change of the coefficient of variation, for example, the rate of change of the coefficient of variation = first coefficient of variation / second coefficient of variation.

[0064] Step S410: Determine the second dispersion index of the battery pack based on the rate of change of the dispersion coefficient and the corresponding second threshold.

[0065] Specifically, the calculated rate of change of the coefficient of variation is compared with a preset second threshold. If the rate of change of the coefficient of variation exceeds the second threshold, it means that the consistency of the battery pack has changed significantly between two time periods, which may be a deterioration or improvement. Based on the comparison result of the rate of change and the second threshold, a second dispersion index of the battery pack is determined. For example, if the monthly rate of change of the coefficient of variation of the battery pack is calculated to be 7.08%, which is greater than the threshold of 6%, then the second dispersion index = monthly rate of change of the coefficient of variation / threshold - 1 = 0.18;

[0066] In this embodiment, by comparing the discrete coefficients over continuous time periods, the deterioration trend of battery pack consistency can be identified early, providing timely warnings and preventing battery pack failures due to increased inconsistency, thus extending the battery pack's lifespan. The calculation of the discrete coefficient change rate provides a quantitative analysis of battery pack state changes, enabling more refined battery pack management and facilitating adjustments to maintenance strategies based on changing trends, such as proactively performing battery equalization or optimizing charging strategies to maintain battery pack consistency.

[0067] In one embodiment, such as Figure 5 As shown, step S210, determining the dispersion index of the battery pack based on the standard deviation and average value of the cell data of multiple cells, further includes steps S500-S510. Wherein:

[0068] Step S500: Determine the correlation coefficient between the discrete coefficient and the cell voltage difference based on the discrete coefficient at each time point within the current preset time period, the average of the discrete coefficients within the current preset time period, the cell voltage difference at each time point within the current preset time period, and the average of the cell voltage difference within the current preset time period.

[0069] Specifically, within the current preset time period, the system records the coefficient of variation and cell voltage difference data at each time point. The correlation coefficient is calculated using statistical methods, such as the Pearson product-moment correlation coefficient, to determine the correlation between the coefficient of variation and the cell voltage difference.

[0070] For example, the formula for the Pearson product-moment correlation coefficient is as follows;

[0071]

[0072] Where, r x,y x is the Pearson correlation coefficient; i The discrete coefficients at the i-th time point are... y is the mean of the discrete coefficients over the current preset time period. i Let be the cell voltage difference at time point i. t represents the average value over the current preset time period.

[0073] Step S510: Determine the third dispersion index of the battery pack based on the correlation coefficient and the corresponding third threshold.

[0074] Specifically, the calculated correlation coefficient is compared with a preset third threshold. If the absolute value of the correlation coefficient exceeds the third threshold, it indicates a significant correlation between the dispersion coefficient and the cell voltage difference. This may mean that an increase in the cell voltage difference leads to an increase in the dispersion coefficient, or vice versa. Based on the strength of this correlation, a third dispersion index for the battery pack is determined to assess whether the consistency of the battery pack is significantly affected by the cell voltage difference. For example, if the Pearson product-moment correlation coefficient of the battery pack is calculated to be 0.89, which is greater than the threshold of 0.7, then the third dispersion index is considered to be = Pearson product-moment correlation coefficient / threshold - 1 = 0.27.

[0075] In this embodiment, by calculating the correlation coefficient between the coefficient of variation and the cell voltage difference, a deeper understanding of the changing trends of the cell states within the battery pack and the impact of these changes on battery consistency can be achieved, leading to more accurate assessments and predictions. The significance of the correlation coefficient indicates the potential negative impact of changes in cell voltage difference on battery pack consistency, helping to identify potential fault risks in the early stages, provide timely warnings, and take preventative measures to avoid a sharp decline in battery pack performance or safety issues.

[0076] In one embodiment, step S220, determining the first evaluation index of the battery pack based on the dispersion index of the battery pack, includes: determining the first evaluation index of the battery pack based on the first dispersion index, the second dispersion index, and the third dispersion index.

[0077] Specifically, the first, second, and third dispersion indices reflect the consistency and changes in the state of the battery cells within the battery pack from the perspectives of comparing the dispersion coefficient with a preset threshold, the trend of the dispersion coefficient, and the correlation between the dispersion coefficient and the cell voltage difference. By weighting and fusing these indices, a quantitative index that comprehensively reflects the current consistency state of the battery pack can be obtained, namely the first evaluation index.

[0078] For example, the first evaluation index = the first dispersion index + the second dispersion index + the third dispersion index. The evaluation result of battery pack consistency under the target operating condition = the first evaluation index + the second evaluation index.

[0079] In this embodiment, the calculation of the first evaluation index integrates multiple indicators that evaluate battery consistency from different perspectives, which can more comprehensively reflect the current consistency status of the battery pack and avoid evaluation bias that may be caused by a single indicator.

[0080] In one embodiment, such as Figure 6 As shown, the battery pack consistency evaluation method further includes steps S600-S610. Wherein:

[0081] Step S600: Determine the evaluation results of the battery pack consistency under multiple different operating conditions.

[0082] Specifically, the current operating condition can be automatically identified based on the vehicle's status (such as whether it is charging or driving). Under each specific operating condition, key parameters such as voltage, current, and temperature of each cell in the battery pack are continuously monitored and recorded. For each operating condition, a first evaluation index for the battery pack under that condition is obtained according to the calculation methods of the aforementioned first, second, and third dispersion indices. This ensures that a battery consistency evaluation is performed under each operating condition, forming a set of operating condition evaluation results.

[0083] For example, the method described in the above embodiments can be used to determine the evaluation results of battery pack consistency under different operating conditions. For example, for the four operating conditions of static, fast charging, slow charging, and driving, the corresponding evaluation results of battery pack consistency can be determined as A, B, C, and D, respectively.

[0084] Step S610: Based on the evaluation results of battery pack consistency under multiple different operating conditions, determine the comprehensive evaluation result of the battery pack consistency to be evaluated.

[0085] Specifically, a weighting factor can be set for each operating condition to reflect its importance in the overall evaluation. The weighting factor can be set based on factors such as the magnitude of its impact on battery life, its frequency of occurrence, and the degree of risk to the battery pack under that condition.

[0086] For example, for the four operating conditions of static, fast charging, slow charging and driving, different weighting factors i, j, m and n are selected respectively to assign different weight values ​​to the consistency index, and then the comprehensive evaluation result V = iA + jB + mC + nD is calculated.

[0087] In this embodiment, by evaluating the battery pack under different operating conditions, the consistency of the battery pack is considered in various usage scenarios, and the changes in battery state under each operating condition are analyzed in detail, thus improving the comprehensiveness and accuracy of the evaluation. Since different operating conditions have different effects on the battery pack, this comprehensive evaluation method can dynamically adapt to the changes in the battery pack state under different scenarios, which helps to adjust vehicle usage strategies or maintenance plans in a timely manner to maintain battery pack consistency and extend battery life.

[0088] In one embodiment, such as Figure 7 As shown, the battery pack consistency evaluation method also includes steps S700-S710. Wherein:

[0089] In step S700, if the comprehensive evaluation result is less than or equal to the maintenance threshold, the battery balancing strategy of the battery pack is adjusted to improve the consistency of the battery pack.

[0090] If the overall evaluation result is less than or equal to the maintenance threshold, and if the overall evaluation result is also less than the balancing threshold, and the balancing threshold is less than the maintenance threshold, then the battery pack consistency meets the standard and no adjustment is needed. Otherwise, the battery pack needs to be balanced. This can be achieved by extending the balancing time, increasing the target workload for balancing, optimizing the balancing current, adjusting the charging / discharging algorithm, or optimizing charging parameters to improve cell consistency. After balancing, the battery pack consistency can be re-evaluated to confirm whether the balancing effect meets the standard.

[0091] Step S710: If the comprehensive evaluation result is greater than the maintenance threshold, a maintenance reminder signal is issued.

[0092] Specifically, when the overall assessment result is within an acceptable range (i.e., less than or equal to the maintenance threshold), it indicates that the battery pack consistency is good or within a controllable range. In this case, the battery balancing strategy can be optimized to further improve the battery pack consistency. However, when the overall assessment result exceeds the maintenance threshold, it means that there is a significant problem with the battery pack consistency, and a maintenance reminder signal needs to be issued to prompt the user or maintenance personnel to check or repair it.

[0093] Specifically, the overall block diagram of the battery pack consistency evaluation method of this application can be found by referring to... Figure 8 , Figure 8 The contents of this application have already been explained and will not be repeated here.

[0094] In this embodiment, by proactively adjusting the battery balancing strategy, action can be taken when the battery pack consistency begins to deteriorate slightly, preventing further deterioration of the cell condition and extending the overall lifespan of the battery pack. When the battery pack consistency is within an acceptable range, optimizing the battery balancing strategy instead of immediately performing repairs can reduce unnecessary costs and time expenditures, avoiding resource waste caused by over-repair. When it is confirmed that the battery pack consistency has deteriorated to the point of requiring repair, timely repair reminders help users detect and perform repairs early, enhancing user confidence in the maintenance of new energy vehicles and battery packs.

[0095] 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 this application, 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 this application.

[0096] This embodiment also provides a battery pack consistency evaluation device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0097] Figure 9 This is a structural block diagram of an optional battery pack consistency evaluation device according to an embodiment of this application. Figure 9 As shown, it includes:

[0098] The data acquisition module 901 is used to acquire cell data of multiple cells of the battery pack to be evaluated under target operating conditions.

[0099] The dispersion determination module 902 is used to determine the dispersion index of the battery pack based on the standard deviation and average value of the cell data of multiple cells.

[0100] The first indicator determination module 903 is used to determine the first evaluation indicator of the battery pack based on the dispersion index of the battery pack.

[0101] The second indicator determination module 904 is used to upload the cell data of multiple cells to the cloud when the first evaluation indicator is determined to be greater than the preset threshold, and to receive the second evaluation indicator of the battery pack returned by the cloud.

[0102] Evaluation module 905 is used to determine the evaluation result of battery pack consistency under target operating conditions based on the first evaluation index and the second evaluation index.

[0103] Through the aforementioned device, on the one hand, by monitoring and analyzing the battery pack's cell data in real time at the vehicle end, the consistency status of the battery pack can be assessed in real time, and potential inconsistencies can be detected in a timely manner, improving the real-time performance of battery pack consistency assessment. On the other hand, for battery packs with poor consistency, cloud-based big data analysis can obtain more comprehensive assessment indicators, improving the accuracy and reliability of the assessment. The battery pack assessment result is determined by combining the first and second assessment indicators evaluated by the vehicle end and the cloud end respectively. By closely integrating cloud and vehicle-side data, the advantages of real-time vehicle-side computing and accuracy cloud computing are fully utilized. The combination of real-time vehicle-side computing and in-depth cloud analysis ensures the real-time and accuracy of battery pack consistency assessment, enabling timely detection and early warning of battery pack consistency issues.

[0104] In an exemplary embodiment, the dispersion determination module 802 is further configured to determine the dispersion coefficient for each time point within the current preset time period based on the standard deviation and average value of the cell data of multiple cells at each time point within the current preset time period. Based on the average value of the dispersion coefficients for each time point within the current preset time period, a first dispersion coefficient for the battery pack to be evaluated is determined. Based on the first dispersion coefficient and the corresponding first threshold, a first dispersion index for the battery pack is determined.

[0105] In an exemplary embodiment, the dispersion determination module 802 is further configured to determine the rate of change of the dispersion coefficient of the battery pack to be evaluated based on the first dispersion coefficient and the second dispersion coefficient of the battery pack to be evaluated within the previous preset time period. Based on the rate of change of the dispersion coefficient and the corresponding second threshold, a second dispersion index of the battery pack is determined.

[0106] In an exemplary embodiment, the first index determination module 803 is further configured to determine a first evaluation index of the battery pack based on a first dispersion index, a second dispersion index, and a third dispersion index.

[0107] In one exemplary embodiment, the above-described apparatus further includes:

[0108] The evaluation result determination module is used to determine the evaluation results of the battery pack consistency under multiple different operating conditions.

[0109] The comprehensive result determination module is used to determine the comprehensive evaluation result of the battery pack consistency to be evaluated based on the evaluation results of battery pack consistency under multiple different operating conditions.

[0110] In one exemplary embodiment, the above-described apparatus further includes:

[0111] The balancing module is used to adjust the battery balancing strategy of the battery pack when the comprehensive evaluation result is less than or equal to the maintenance threshold, so as to improve the consistency of the battery pack.

[0112] The reminder module is used to issue a maintenance reminder signal when the comprehensive evaluation result exceeds the maintenance threshold.

[0113] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.

[0114] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:

[0115] S1, acquire cell data of multiple cells of the battery pack to be evaluated under target operating conditions.

[0116] S2 determines the dispersion index of the battery pack based on the standard deviation and average value of the cell data of multiple cells.

[0117] S3. Determine the first evaluation index of the battery pack based on the dispersion index of the battery pack.

[0118] S4, if the first evaluation index is determined to be greater than the preset threshold, upload the cell data of multiple cells to the cloud and receive the second evaluation index of the battery pack returned by the cloud.

[0119] S5. Based on the first evaluation index and the second evaluation index, determine the evaluation result of battery pack consistency under the target operating condition.

[0120] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0121] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0122] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0123] S1, acquire cell data of multiple cells of the battery pack to be evaluated under target operating conditions.

[0124] S2 determines the dispersion index of the battery pack based on the standard deviation and average value of the cell data of multiple cells.

[0125] S3. Determine the first evaluation index of the battery pack based on the dispersion index of the battery pack.

[0126] S4, if the first evaluation index is determined to be greater than the preset threshold, upload the cell data of multiple cells to the cloud and receive the second evaluation index of the battery pack returned by the cloud.

[0127] S5. Based on the first evaluation index and the second evaluation index, determine the evaluation result of battery pack consistency under the target operating condition.

[0128] Optionally, in this embodiment, the storage medium may include, but is not limited to, 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.

[0129] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium storing the computer program product, wherein the computer program, when executed by a processor, implements the steps of the methods in various embodiments of this application.

[0130] Optionally, in this embodiment, the computer program described above can be configured to perform the following steps when executed by a processor:

[0131] S1, acquire cell data of multiple cells of the battery pack to be evaluated under target operating conditions.

[0132] S2 determines the dispersion index of the battery pack based on the standard deviation and average value of the cell data of multiple cells.

[0133] S3. Determine the first evaluation index of the battery pack based on the dispersion index of the battery pack.

[0134] S4, if the first evaluation index is determined to be greater than the preset threshold, upload the cell data of multiple cells to the cloud and receive the second evaluation index of the battery pack returned by the cloud.

[0135] S5. Based on the first evaluation index and the second evaluation index, determine the evaluation result of battery pack consistency under the target operating condition.

[0136] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0137] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0138] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for evaluating battery pack consistency, characterized in that, The method comprises: obtaining cell data of a plurality of cells of a battery pack to be evaluated under a target working condition; determining a dispersion degree index of the battery pack according to a standard deviation and an average value of the cell data of the plurality of cells; determining a first evaluation index of the battery pack according to the dispersion degree index of the battery pack; in a case where the first evaluation index is greater than a preset threshold, uploading the cell data of the plurality of cells to a cloud end, and receiving a second evaluation index of the battery pack returned by the cloud end; determining an evaluation result of consistency of the battery pack under the target working condition according to the first evaluation index and the second evaluation index.

2. The method of evaluating battery pack uniformity according to claim 1, wherein The determining of the dispersion degree index of the battery pack according to the standard deviation and the average value of the cell data of the plurality of cells comprises: determining a dispersion coefficient of each time point in a current preset time period according to a standard deviation and an average value of the cell data of the plurality of cells at each time point in the current preset time period; determining a first dispersion coefficient of the battery pack to be evaluated according to an average value of the dispersion coefficient of each time point in the current preset time period; determining a first dispersion degree index of the battery pack according to the first dispersion coefficient and a corresponding first threshold.

3. The method of evaluating battery pack uniformity according to claim 2, wherein The determining of the dispersion degree index of the battery pack according to the standard deviation and the average value of the cell data of the plurality of cells further comprises: determining a dispersion coefficient change rate of the battery pack to be evaluated according to the first dispersion coefficient and a second dispersion coefficient of the battery pack to be evaluated in a previous preset time period; determining a second dispersion degree index of the battery pack according to the dispersion coefficient change rate and a corresponding second threshold.

4. The method of evaluating battery pack uniformity according to claim 3, wherein The determining of the dispersion degree index of the battery pack according to the standard deviation and the average value of the cell data of the plurality of cells further comprises: determining a correlation coefficient between the dispersion coefficient and a cell pressure difference according to the dispersion coefficient of each time point in the current preset time period, the average value of the dispersion coefficient in the current preset time period, the cell pressure difference of each time point in the current preset time period, and the average value of the cell pressure difference in the current preset time period; determining a third dispersion degree index of the battery pack according to the correlation coefficient and a corresponding third threshold.

5. The method of evaluating battery pack uniformity according to claim 4, wherein The determining of the first evaluation index of the battery pack according to the dispersion degree index of the battery pack comprises: determining the first evaluation index of the battery pack according to the first dispersion degree index, the second dispersion degree index, and the third dispersion degree index.

6. The method of evaluating battery pack uniformity according to any one of claims 1-4, wherein The method further comprises: determining evaluation results of consistency of the battery pack to be evaluated under a plurality of different working conditions; and determining a comprehensive evaluation result of consistency of the battery pack to be evaluated according to the evaluation results of consistency of the battery pack under the plurality of different working conditions.

7. The method of evaluating battery pack uniformity according to claim 6, wherein The method further comprises: in a case where the comprehensive evaluation result is less than or equal to a maintenance threshold, adjusting a battery equalization strategy of the battery pack to improve the consistency of the battery pack; in a case where the comprehensive evaluation result is greater than the maintenance threshold, issuing a maintenance reminding signal.

8. A battery pack consistency evaluation device, characterized in that, The method comprises: The data acquisition module is configured to acquire cell data of a plurality of cells of a battery pack to be evaluated under a target working condition; The dispersion degree determination module is configured to determine a dispersion degree index of the battery pack according to a standard deviation and an average value of the cell data of the plurality of cells; The first index determination module is configured to determine a first evaluation index of the battery pack according to the dispersion degree index of the battery pack; The second index determination module is configured to upload the cell data of the plurality of cells to a cloud end and receive a second evaluation index of the battery pack returned by the cloud end in a case where the first evaluation index is greater than a preset threshold. The evaluation module is configured to determine an evaluation result of consistency of the battery pack under the target working condition according to the first evaluation index and the second evaluation index.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, wherein the computer program is executed by the processor to implement the steps of the method in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1-7.

11. A computer program product, characterised in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1-7.

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