Identification and evaluation method and device for abnormal attenuation outlier of power battery, medium, program product and terminal

Abstract

The invention provides a power battery abnormal attenuation outlier identification and evaluation method and device, a medium, a program product and a terminal, and the method comprises the steps: obtaining the accumulated discharge amount and use time of a plurality of batteries, carrying out the grouping of the data according to a preset fixed interval, and building a preliminary comparison basis. Then, secondary grouping is carried out based on discharge change parameters, and potential abnormal data points are finely focused; and finally, identifying the abnormally attenuated power battery through outlier analysis. According to the method, the identification accuracy is remarkably improved through multi-level grouping and fine analysis. Different from a traditional method depending on a single data index, noise caused by individual use differences is effectively shielded by combining a double grouping mechanism of accumulated discharge capacity and use duration. In addition, the combination of secondary grouping and outlier analysis shows higher sensitivity in the aspect of identifying early attenuation batteries, and is helpful for finding problems in advance and improving battery management and maintenance strategies.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, device, medium, program product and terminal for identifying and evaluating abnormal attenuation outliers of power batteries. Background Art

[0002] As global awareness of sustainable development and environmental protection increases, the new energy industry has ushered in rapid development. Electric vehicles (EVs), as a clean energy means of transportation, are receiving widespread attention and support. Among the core technologies of electric vehicles, the power battery system plays a vital role. Its main function is to efficiently store and release electrical energy to meet the energy needs of the vehicle during driving. However, after multiple charge and discharge cycles and long-term use, power batteries often experience capacity decay and performance degradation. This decay phenomenon will not only lead to a reduction in battery life and a longer charging time, but may also limit the user's travel range and increase the frequency of charging, thereby affecting the user's overall experience.

[0003] What is more serious is that the rapid degradation of the battery may cause it to be scrapped prematurely, increasing the economic burden on users. At the same time, as the battery capacity decreases and the internal resistance increases, the thermal management problem of the battery becomes more prominent, increasing safety risks. Therefore, the impact of battery capacity degradation involves multiple aspects such as user experience, usage cycle, economy and safety, and has become a major problem in the promotion of electric vehicles. At present, the existing battery monitoring and management technologies still have certain defects in quickly identifying and locating abnormal degradation, and often cannot provide accurate data support in a timely manner, making it difficult for users to effectively respond to changes in battery performance, thereby affecting the overall safety and reliability of electric vehicles. Summary of the invention

[0004] In view of the shortcomings of the prior art mentioned above, the purpose of the present application is to provide a method, device, medium, program product and terminal for identifying and evaluating abnormal attenuation outliers of power batteries, so as to solve the problem that the existing battery monitoring and management technologies lack accuracy and timeliness in quickly identifying and locating abnormal attenuation, and cannot effectively support users in coping with changes in battery performance.

[0005] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides a method for identifying and evaluating power battery abnormal attenuation outliers, the method comprising: obtaining the cumulative discharge capacity and corresponding usage time of multiple power batteries; dividing the cumulative discharge capacity and usage time respectively according to preset fixed intervals to generate multiple groups; allocating each power battery to a corresponding group according to its corresponding cumulative discharge capacity and usage time, thereby forming a first grouping result containing multiple groups; performing a secondary grouping operation on the first grouping result based on a discharge change parameter to generate a second grouping result; performing an outlier analysis operation on the second grouping result to identify and extract power batteries with abnormal attenuation outliers.

[0006] In some embodiments of the first aspect of the present application, the multiple power batteries are of the same model and have the same rated energy.

[0007] In some embodiments of the first aspect of the present application, based on the discharge change parameters, the process of performing a secondary grouping operation on the first grouping result includes: obtaining the discharge change parameters of each power battery; grouping the discharge change parameters according to preset standards, and re-grouping each group in the first grouping result according to the grouped discharge change parameters.

[0008] In some embodiments of the first aspect of the present application, the discharge variation parameter includes: one of an average daily discharge cycle and a battery discharge temperature.

[0009] In some embodiments of the first aspect of the present application, the process of performing an outlier analysis operation on the second grouping results includes: for each group in the second grouping, calculating the SOH distribution diagram of each group of batteries according to the attenuation value in each group, and identifying and extracting power batteries with abnormal attenuation outliers from each group in the second group according to the SOH distribution diagram.

[0010] In some embodiments of the first aspect of the present application, the process of identifying and extracting abnormally attenuated outlier power batteries from each group in the second grouping according to the SOH distribution diagram includes: calculating the quartiles and interquartile ranges of the SOH distribution diagram; identifying outliers in the SOH distribution diagram based on the quartiles and interquartile ranges, and the power batteries identified as outliers are power batteries with abnormal attenuation.

[0011] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides an identification and evaluation device for abnormal attenuation outliers of power batteries, including: a battery data acquisition module: used to obtain the cumulative discharge capacity and corresponding usage time of multiple power batteries; a grouping result generation module: used to divide the cumulative discharge capacity and usage time according to preset fixed intervals to generate multiple groups; each power battery is assigned to a corresponding group according to its corresponding cumulative discharge capacity and usage time, thereby forming a first grouping result containing multiple groups; a secondary grouping operation is performed on the first grouping result based on a discharge change parameter to generate a second grouping result; an abnormal analysis and identification module: used to perform an outlier analysis operation on the second grouping result to identify and extract power batteries with abnormal attenuation outliers.

[0012] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying and evaluating abnormal attenuation outliers of a power battery.

[0013] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides a computer program product, which includes a computer program code. When the computer program code is run on a computer, the computer implements the method for identifying and evaluating the abnormal attenuation outliers of the power battery.

[0014] To achieve the above-mentioned purpose and other related purposes, the fifth aspect of the present application provides an electronic terminal, including a memory, a processor and a computer program stored in the memory; the processor executes the computer program to implement the method for identifying and evaluating the abnormal attenuation outliers of the power battery.

[0015] As described above, the identification and evaluation method, device, medium, program product and terminal of abnormal attenuation outliers of power batteries of the present application have the following beneficial effects: The scheme of the present application improves the accuracy of identifying abnormal attenuation outliers of power batteries through multi-level grouping and sophisticated analysis strategies. Different from the traditional method that relies on a single data indicator or a single analysis dimension, this method combines the dual grouping mechanism of cumulative discharge amount and usage time, which can more effectively shield the noise caused by individual usage differences. In addition, the combination of secondary grouping and outlier analysis shows higher sensitivity and accuracy in identifying early attenuation batteries, which helps to detect problems in advance and improve battery management and maintenance strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart in an embodiment of a method for identifying and evaluating abnormal attenuation outliers of a power battery of the present application is shown.

[0017] Figure 2The SOH distribution diagram for more than one cycle in an embodiment of the method for identifying and evaluating abnormal attenuation outliers of a power battery of the present application is shown.

[0018] Figure 3 The SOH distribution diagram from 1 to 2 cycles in an embodiment of the method for identifying and evaluating abnormal attenuation outliers of a power battery of the present application is shown.

[0019] Figure 4 The SOH distribution diagram of more than 2 cycles in an embodiment of the method for identifying and evaluating abnormal attenuation outliers of a power battery of the present application is shown.

[0020] Figure 5 A structural schematic diagram of an embodiment of a device for identifying and evaluating abnormal attenuation outliers of a power battery of the present application is shown.

[0021] Figure 6 A structural schematic diagram of an embodiment of an identification and evaluation terminal for abnormal attenuation outliers of power batteries of the present application is shown. DETAILED DESCRIPTION

[0022] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0023] Before further explaining the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations:

[0024] <1> Cumulative discharge capacity: refers to the total amount of electrical energy released by a battery within a certain period of time, usually measured in ampere-hours (Ah) or watt-hours (Wh). It reflects the battery's usage and energy output capacity.

[0025] <2> Battery life: refers to the cumulative time from the beginning of battery use to the present, usually in hours or days. This indicator helps to evaluate the battery life and efficiency.

[0026] <3> Outlier analysis: A statistical method used to identify and analyze abnormal values ​​or outliers in a data set that are significantly different from other data points. This can help identify signs of failure or performance degradation in battery performance monitoring.

[0027] <4> Discharge variation parameters: refers to various factors that affect the discharge characteristics and performance during the battery discharge process, such as current changes, temperature changes, discharge time, etc.

[0028] <5> Average daily discharge cycles: refers to the average number of times a battery is discharged in a day, usually used to evaluate the battery's usage frequency and load conditions.

[0029] <6> Battery discharge temperature: refers to the temperature of the battery during the discharge process. Temperature has an important impact on the performance and safety of the battery. Too high or too low a temperature may cause the battery to reduce efficiency or be damaged.

[0030] <7> SOH (State of Health): Measured by comparing the current battery capacity, internal resistance and other performance parameters with its design specifications. A higher SOH value indicates that the battery is in good working condition and can effectively store and release energy, while a lower SOH value indicates that the battery may be attenuated or damaged, affecting its normal use.

[0031] <8> Z-quantile method: Z-quantile method is a statistical method that divides a data set into several equal parts through the Z-score standardization process, which is used for descriptive statistics and outlier detection. Z-score calculates the standardized distance between the data point and the mean, and divides the data points into different intervals based on this value to identify the distribution characteristics and variability of the data, and is also used to determine outliers.

[0032] To facilitate understanding of the embodiments of the present application, first Figure 1 Detailed description. Figure 1 A flow chart of a method for identifying and evaluating abnormal power battery attenuation outliers in an embodiment of the present application is shown. The method for identifying and evaluating abnormal power battery attenuation outliers in this embodiment mainly includes the following steps:

[0033] Step S11: Obtain the accumulated discharge amounts and corresponding usage durations of multiple power batteries.

[0034] In one embodiment of the present application, the multiple power batteries are of the same model and have the same rated energy.

[0035] In this embodiment, multiple power batteries of the same model are used to monitor and screen the performance of these batteries. The consistency of technical specifications and performance of all batteries enables the system to more effectively identify batteries with abnormal attenuation, so that individuals with poor performance can be accurately screened out from a large number of batteries through this application to improve the overall operating efficiency and safety of the system.

[0036] Step S12: Divide the accumulated discharge amount and the usage time respectively according to preset fixed intervals to generate multiple groups; assign each power battery to a corresponding group according to its corresponding accumulated discharge amount and usage time, thereby forming a first grouping result containing multiple groups; perform a secondary grouping operation on the first grouping result based on the discharge change parameter to generate a second grouping result.

[0037] In one embodiment of the present application, based on the main influencing factors of battery attenuation, the power batteries are grouped for the first time, and the process of forming a first group containing multiple groups includes: dividing the cumulative discharge amount of the power battery into multiple intervals, for example, every 50,000 kWh as an interval. In addition, the time from the power battery leaving the factory to the deadline is also divided into multiple intervals, for example, every 6 months as an interval. Through the combination of these two division methods, a unique interval can be determined for each set of power batteries. It should be noted that the above-mentioned 50,000 kWh and 6 months are only examples, and do not limit the value of the preset interval. The specific cumulative discharge amount interval width can be adjusted according to actual needs. Table 1 shows the first grouping results in this embodiment.

[0038]

[0039] Table 1: Results of the first grouping of power batteries

[0040] In one embodiment of the present application, based on the discharge change parameter, the process of performing a secondary grouping operation on the first grouping result includes: obtaining the discharge change parameter of each power battery; grouping the discharge change parameter according to a preset standard, and re-grouping each group in the first grouping result according to the grouped discharge change parameter.

[0041] In this embodiment, based on the discharge change parameter, the process of performing a secondary grouping operation on the first grouping result includes the following steps: obtaining the average daily discharge cycles of each power battery, and dividing the average daily discharge cycles into three groups: less than 1 cycle, 1 to 2 cycles, and more than 2 cycles. Optionally, the discharge temperature of each power battery is obtained, and the discharge temperature is divided into high-temperature batteries, normal-temperature batteries, and low-temperature batteries. For example, taking the battery cumulative discharge capacity (kwh): 15w-20w; 1056 sets of batteries corresponding to the interval of delivery time [901 days, 1080 days] as an example, secondary grouping is performed on them, and Table 2 shows the number of batteries corresponding to different average daily discharge cycles of the 1056 sets of batteries.

[0042] Number of batteries Within 1 lap 438 1 to 2 laps 628 2 laps or more 330

[0043] Table 2: Results of the second grouping of power batteries

[0044] In one embodiment of the present application, the discharge variation parameter includes: one of the average daily discharge cycles and the battery discharge temperature. In this embodiment, the discharge variation parameter also includes the number of charging cycles, the charging method, and the current intensity. Among them, the number of charging cycles includes low cycles (1-100 times), medium cycles (101-500 times) and high cycles (more than 500 times); the charging method can be divided into fast charging, standard charging and slow charging; the current intensity and discharge rate can be subdivided into low current (<1C), medium current (1C-3C) and high current (>3C).

[0045] Step S13: performing an outlier analysis operation on the second grouping result to identify and extract power batteries with abnormal attenuation and outliers.

[0046] In one embodiment of the present application, the process of performing an outlier analysis operation on the second grouping results includes: for each group in the second grouping, calculating the SOH distribution diagram of each group of batteries according to the attenuation value in each group, and identifying and extracting power batteries with abnormal attenuation outliers from each group in the second group according to the SOH distribution diagram.

[0047] In this embodiment, in the battery capacity decay analysis, outlier analysis is performed by the Z-quantile method, including the following steps: obtaining the capacity decay values ​​of the same group of batteries, and calculating their mean (μ) and standard deviation (σ). The Z value of each decay value is calculated by formula 1 to measure the deviation from the mean. The Z value represents the distance of each data point from the mean in units of standard deviation. Exemplarily, Z values ​​greater than 2 are regarded as outliers. In a normally distributed data set, approximately 95% of the data points have Z scores between -2 and 2, and approximately 99.7% of the data points have Z scores between -3 and 3. Therefore, when the absolute value of the Z value is greater than 2, the data point is regarded as an outlier and marked as abnormal decay.

[0048]

[0049] It should be noted that the outlier analysis method of the present application is intended to ensure the accuracy and reliability of the data. On this basis, it is assumed that the decay process of the power battery conforms to the normal distribution. This assumption provides a solid theoretical basis for the judgment of outliers. Although the Z value and standard deviation of small sample data may have certain instabilities, affecting the identification of outliers, the data characteristics of power battery decay effectively overcome this challenge. The decay pattern of the power battery does follow the normal distribution, which not only enhances the accuracy of outlier judgment, but also highlights the advantages of this application in power battery monitoring and management. By accurately identifying outliers, potential battery performance problems can be discovered early, thereby optimizing the battery's service life and safety, which is of great significance to promoting the development of electric vehicles and clean energy technologies.

[0050] In one embodiment of the present application, the process of identifying and extracting abnormally attenuated outlier power batteries from each group in the second group according to the SOH distribution graph includes: calculating the quartiles and interquartile range of the SOH distribution graph; identifying outliers in the SOH distribution graph based on the quartiles and interquartile range, and the power batteries identified as outliers are abnormally attenuated power batteries.

[0051] In this embodiment, the process identifies and extracts power batteries with abnormal performance through statistical methods. First, data is extracted from the SOH (state of health) distribution diagram, and the quartiles (Q1, Q2, Q3) and interquartile range (IQR, Interquartile Range, IQR) are calculated to measure the degree of dispersion of the data. Then, the outlier criteria are defined based on the quartiles and interquartile ranges, and data points below Q1-1.5*IQR or above Q3+1.5*IQR are identified as outliers. Power batteries judged as outliers are regarded as abnormally attenuated batteries, and their health status is significantly lower than the normal range.

[0052] Among them, the quartiles divide the data set into four parts, namely Q1, Q2 (median) and Q3. Q1 is the 25th percentile, which means that 25% of the values ​​in the data are lower than this value; Q2 is the median, that is, the midpoint of the data; Q3 is the 75th percentile, which means that 75% of the values ​​are lower than this value. The interquartile range is the difference between Q3 and Q1 (IQR=Q3-Q1), which is used to measure the dispersion of the middle 50% of the data. Through this process, abnormal batteries can be effectively identified, thereby improving management and maintenance efficiency and ensuring the safety and reliability of the battery system. Table 3 shows the capacity attenuation values ​​of the power batteries in each group in this embodiment, and the SOH distribution of each group of batteries is obtained based on the attenuation values, and then outliers are identified through the SOH distribution:

[0053]

[0054] Table 3: Outlier analysis results of abnormal battery attenuation outliers

[0055] to Figure 4 The SOH distribution diagrams of the first to third groups in Table 3 are shown. By grouping the batteries, abnormal batteries can be identified more accurately. For example, the same SOH value of 87 will be judged as outlier data in the first and second groups, but it will be considered normal data in the third group. This grouping method based on factors affecting battery attenuation makes it more accurate to judge whether the data attenuation is abnormal, thereby effectively improving the ability to identify abnormal batteries.

[0056] In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same items or similar items with substantially the same functions and effects. For example, the first group and the second group are only used to distinguish different groups, and do not limit their order. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0057] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0058] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0059] Figure 5 is a schematic block diagram of a device for identifying and evaluating abnormal attenuation of a power battery provided in an embodiment of the present application. Figure 5 As shown, the device includes a battery data acquisition module 501, a grouping result generation module 502 and an abnormality analysis and identification module 503.

[0060] Battery data acquisition module 501: used to obtain the cumulative discharge amount and corresponding usage time of multiple power batteries;

[0061] The grouping result generating module 502 is used to divide the accumulated discharge amount and the use time into multiple groups according to preset fixed intervals; allocate each power battery to a corresponding group according to its corresponding accumulated discharge amount and use time, thereby forming a first grouping result including multiple groups; perform a secondary grouping operation on the first grouping result based on the discharge change parameter to generate a second grouping result;

[0062] The abnormal analysis and identification module 503 is used to perform an outlier analysis operation on the second grouping result to identify and extract the power batteries with abnormal attenuation outliers.

[0063] It should be understood that the specific process of each module executing the above corresponding steps has been described in detail in the above method embodiment, and for the sake of brevity, it will not be repeated here.

[0064] It should also be understood that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0065] Figure 6 is a schematic block diagram of an electronic terminal provided in an embodiment of the present application. Figure 6 As shown, the electronic terminal includes: at least one processor 601, a memory 602, at least one network interface 603 and a user interface 605. The various components in the device are coupled together through a bus system 604. It can be understood that the bus system 604 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 604 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 6 In the specification, various buses are labeled as bus systems.

[0066] The user interface 605 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0067] It is understood that the memory 602 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of exemplary but not limiting explanation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present application is intended to include but is not limited to these and any other suitable categories of memory.

[0068] The memory 602 in the embodiment of the present application is used to store various categories of data to support the operation of the electronic terminal 600. Examples of these data include: any executable program for operating on the electronic terminal 600, such as an operating system 6021 and an application 6022; the operating system 6021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 6022 may include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The method for identifying and evaluating the abnormal attenuation outliers of the power battery provided in the embodiment of the present application may be included in the application 6022.

[0069] The method disclosed in the above embodiment of the present application can be applied to the processor 601, or implemented by the processor 601. The processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 601 or an instruction in the form of software. The above processor 601 may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 601 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor 601 may be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0070] In an exemplary embodiment, the electronic terminal 600 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0071] According to the method provided in the embodiments of the present application, the present application also provides a computer program product, which includes: computer program code, when the computer program code is run on a computer, the computer executes the method for identifying and evaluating abnormal attenuation outliers of power batteries in any of the embodiments shown above.

[0072] According to the method provided in the embodiments of the present application, the present application also provides a computer-readable storage medium, which stores a program code. When the program code runs on a computer, the computer executes the method for identifying and evaluating abnormal attenuation outliers of power batteries in any of the embodiments shown above.

[0073] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program and / or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0074] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0076] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0079] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When loading and executing computer program instructions (programs) on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more available media integrated. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., high-density digital video discs (DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0080] If the function is implemented in the form of 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 application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0081] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0082] In summary, the present application provides an identification and evaluation method, device, medium, program product and terminal for abnormal attenuation outliers of power batteries. The present application proposes an improved method for identifying abnormal attenuation of power batteries. The cumulative discharge amount and usage time of multiple batteries are obtained, and these data are grouped at preset fixed intervals to generate preliminary grouping results, thereby establishing a relative comparison basis between batteries. The preliminary grouping results are subjected to secondary grouping operations based on the discharge change parameters, so that potential abnormal data points can be focused more finely. The power battery with abnormal attenuation is identified by outlier analysis. The present application significantly improves the accuracy of identifying abnormal attenuation outliers of power batteries through multi-level grouping and fine analysis strategies. Compared with the traditional method that relies only on a single data indicator, the present application combines a dual grouping mechanism of cumulative discharge amount and usage time to more effectively shield the noise caused by individual usage differences. In addition, the combination of secondary grouping and outlier analysis shows higher sensitivity and accuracy in identifying early attenuation batteries, which helps to find problems in advance and improve battery management and maintenance strategies. Therefore, the present application effectively overcomes the various shortcomings of the prior art and has a high industrial utilization value.

[0083] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for identifying and evaluating abnormal attenuation outliers of power batteries, characterized in that: The method comprises: Obtain the cumulative discharge capacity and corresponding usage time of multiple power batteries; The accumulated discharge amount and the usage time are divided into a plurality of groups according to preset fixed intervals; each power battery is assigned to a corresponding group according to its corresponding accumulated discharge amount and usage time, thereby forming a first grouping result including a plurality of groups; a secondary grouping operation is performed on the first grouping result based on the discharge change parameter to generate a second grouping result; An outlier analysis operation is performed on the second grouping result to identify and extract power batteries with abnormal attenuation and outliers.

2. The method for identifying and evaluating abnormal attenuation outliers of power batteries according to claim 1, characterized in that: The multiple power batteries are all of the same model and have the same rated energy.

3. The method for identifying and evaluating abnormal attenuation outliers of a power battery according to claim 2, characterized in that: Based on the discharge variation parameters, the process of performing a secondary grouping operation on the first grouping result includes: obtaining the discharge variation parameters of each power battery; grouping the discharge variation parameters according to preset standards, and re-grouping each group in the first grouping result according to the grouped discharge variation parameters.

4. The method for identifying and evaluating abnormal attenuation outliers of a power battery according to claim 3, characterized in that: The discharge variation parameter includes: one of an average daily discharge cycle number and a battery discharge temperature.

5. The method for identifying and evaluating abnormal attenuation outliers of a power battery according to claim 2, characterized in that: The process of performing an outlier analysis operation on the second grouping results includes: for each group in the second grouping, calculating the SOH distribution diagram of each group of batteries according to the attenuation value in each group, and identifying and extracting power batteries with abnormal attenuation outliers from each group in the second grouping according to the SOH distribution diagram.

6. The method for identifying and evaluating abnormal attenuation outliers of a power battery according to claim 5, characterized in that: The process of identifying and extracting abnormally attenuated outlier power batteries from each group in the second group according to the SOH distribution diagram includes: calculating the quartiles and interquartile range of the SOH distribution diagram; identifying outliers in the SOH distribution diagram based on the quartiles and interquartile range, and the power batteries identified as outliers are abnormally attenuated power batteries.

7. A device for identifying and evaluating abnormal attenuation of a power battery, characterized in that: include: Battery data acquisition module: used to obtain the cumulative discharge capacity and corresponding usage time of multiple power batteries; A grouping result generation module: used to divide the accumulated discharge amount and the usage time into multiple groups according to preset fixed intervals; Allocating each power battery to a corresponding group according to its corresponding accumulated discharge amount and usage time, thereby forming a first grouping result including a plurality of groups; performing a secondary grouping operation on the first grouping result based on the discharge variation parameter to generate a second grouping result; Abnormal analysis and identification module: used to perform outlier analysis on the second grouping result to identify and extract power batteries with abnormal attenuation outliers.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying and evaluating abnormal attenuation outliers of a power battery as described in any one of claims 1 to 6 is implemented.

9. A computer program product, characterized in that The computer program product includes computer program codes, and when the computer program codes are executed on a computer, the computer is enabled to implement the method for identifying and evaluating abnormal attenuation outliers of a power battery according to any one of claims 1 to 6.

10. An electronic terminal comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method for identifying and evaluating abnormal attenuation outliers of a power battery as described in any one of claims 1 to 6.

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