A battery consistency evaluation method, device, storage medium and equipment

Abstract

The present application relates to the technical field of battery evaluation, and in particular to a battery consistency evaluation method, device, storage medium and equipment. The method comprises: obtaining performance parameter data in the running process of the battery; determining a time operator representing the state and use of the battery according to the performance parameter data; calculating sample entropy according to the performance parameter data in a sliding window determined by the time operator; and determining the consistency of the battery according to the relationship between the sample entropy and a threshold value determined by the time operator. In this embodiment, the sliding window size and the threshold value are dynamically adjusted by the time operator, so that the dynamic changes of the battery performance can be more accurately captured. Inconsistent battery groups can be found in the early stage of battery use, and false positives or false negatives caused by improper fixed parameter settings can be effectively avoided. Meanwhile, the combination of the time operator and the sample entropy algorithm can more comprehensively reflect the real situation of the battery performance, and the evaluation results are more accurate and reliable, especially when dealing with nonlinear and non-normal distribution data.

Description

Technical Field

[0001] This invention relates to the field of battery evaluation technology, and specifically to a battery conformity evaluation method, apparatus, storage medium, and device. Background Technology

[0002] Currently, there are various assessment methods for battery consistency evaluation. These include statistical methods, which use averages or standard deviations to assess battery consistency. However, averages and standard deviations may not fully reflect the true performance of the battery, especially for non-linear or non-normally distributed data. These methods cannot accurately capture subtle changes or specific patterns in battery performance, thus affecting the accuracy of the evaluation results. Another method is based on charge-discharge curves. While charge-discharge curves provide an intuitive understanding of battery performance changes, they typically require complex data processing and analysis techniques and have high data quality requirements. Furthermore, the analysis of charge-discharge curves may be affected by measurement errors, environmental conditions, and other factors, leading to uncertainty in the evaluation results. Finally, there is the Shannon entropy-based consistency evaluation method. While Shannon entropy can quantify the uncertainty and complexity of data, it may be overly sensitive in some cases, easily affected by outliers or noise, leading to unstable evaluation results. Moreover, although the Shannon entropy method is effective, it usually requires a relatively uniform data distribution. In cases of uneven data distribution or insufficient data volume, this method may be unsuitable, thus affecting the reliability of the evaluation results.

[0003] Therefore, current methods for evaluating battery consistency have certain limitations and cannot achieve accurate evaluation of battery consistency. Summary of the Invention

[0004] In view of this, the present invention provides a battery consistency evaluation method, apparatus, storage medium and device to solve the problem that current battery consistency evaluation methods have certain limitations.

[0005] In a first aspect, the present invention provides a battery consistency evaluation method, the method comprising: acquiring performance parameter data during battery operation; determining a time operator characterizing battery state and usage based on the performance parameter data; calculating sample entropy based on the performance parameter data within a sliding window determined by the time operator; and determining battery consistency based on the relationship between the sample entropy and a threshold determined by the time operator.

[0006] The battery consistency evaluation method provided in this invention collects battery performance parameter data in real time and determines a time operator accordingly. The time operator dynamically adjusts the sliding window size and threshold, thereby more accurately capturing dynamic changes in battery performance. This method can promptly identify inconsistent battery packs in the early stages of battery use, effectively avoiding misjudgments or omissions caused by improper fixed parameter settings. Furthermore, combining the time operator and sample entropy algorithm can more comprehensively reflect the true state of battery performance, especially when dealing with nonlinear and non-normally distributed data, where the evaluation results are more accurate and reliable. Moreover, compared to approximate entropy, sample entropy may better represent the entropy of the system. The advantage of this method is its ability to classify complex systems containing both fixed and random variables, achieving effective evaluation even with limited data points. In addition, the algorithm framework based on dynamic windows and sample entropy has broad applicability, suitable for various types of battery consistency evaluation, providing new ideas and methods for the field of battery consistency evaluation.

[0007] In one optional implementation, determining a time operator characterizing battery state and usage based on performance parameter data includes: calculating the battery's health state based on performance parameters; calculating the charge-discharge curve similarity based on performance parameters; and weighting the health state and the charge-discharge curve similarity to calculate the time operator.

[0008] In this embodiment, the time operator is calculated based on the weighted average of the battery's health status and charge / discharge curve similarity, providing a data foundation for the calculation of the time operator.

[0009] In one alternative implementation, the time operator is calculated using the following formula:

[0010] In the formula, α and β are weighting coefficients, and SOH represents the health status.

[0011] In one alternative implementation, the sliding window and the threshold are calculated using the following formulas: m=k1 / TO

[0012] In the formula, TO represents the time operator, m represents the sliding window, TH represents the threshold, and k1 and k2 are scaling coefficients.

[0013] In this embodiment, the sliding window is calculated inversely proportional to the time operator, and the threshold is calculated directly proportional to the time operator. Therefore, a smaller time operator results in a larger window to capture more performance parameter changes, while a larger time operator results in a smaller window to reduce unnecessary computation. Simultaneously, a larger time operator results in a higher threshold to reduce false alarms, while a smaller time operator results in a lower threshold to improve sensitivity.

[0014] In one alternative implementation, within a sliding window determined by a time operator, calculating sample entropy based on performance parameter data includes: dividing the performance parameter data into multiple subsequences according to the sliding window determined by the time operator; calculating the sample entropy of each subsequence using a sample entropy algorithm; and calculating the overall sample entropy of the battery based on the sample entropy of each subsequence.

[0015] In this embodiment, the sample entropy of each subsequence is calculated by dividing it into subsequences, and the comprehensive sample entropy is determined by weighting the sample entropy of the subsequences. This allows for a more flexible assessment of battery consistency over different time periods and saves the computational cost of real-time evaluation.

[0016] In one optional implementation, determining battery consistency based on the relationship between sample entropy and a threshold determined by a time operator includes: classifying the battery consistency based on sample entropy and a threshold determined by a time operator to obtain a consistency evaluation result.

[0017] In this embodiment, the consistency of the battery is evaluated by classifying the battery according to the sample entropy and threshold, which can more accurately determine the consistency result of the battery.

[0018] In one optional implementation, after acquiring the performance parameter data during battery operation, the method further includes: cleaning the performance parameters to obtain cleaned data; filtering the cleaned data to obtain processed data; and standardizing the processed data to obtain standardized data.

[0019] In this embodiment, by cleaning, filtering and standardizing the data, outliers, erroneous data and noise interference are effectively eliminated, thereby improving the stability and reliability of the evaluation results.

[0020] Secondly, the present invention provides a battery consistency evaluation device, the device comprising: a data acquisition module for acquiring performance parameter data during battery operation; a time operator determination module for determining a time operator characterizing the battery state and usage based on the performance parameter data; a sample entropy calculation module for calculating sample entropy based on the performance parameter data within a sliding window determined by the time operator; and an evaluation module for determining battery consistency based on the relationship between the sample entropy and a threshold determined by the time operator.

[0021] Thirdly, the present invention provides a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the battery consistency evaluation method of the first aspect or any corresponding embodiment described above.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the battery conformity assessment method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the battery consistency evaluation method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating another battery consistency evaluation method according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a battery consistency evaluation device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

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

[0027] This embodiment provides a battery consistency evaluation method, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 1 This is a flowchart of a battery consistency evaluation method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Acquire performance parameter data during battery operation. Specifically, the performance parameter data includes voltage, current, temperature, and capacity data during battery operation. This performance parameter data can be collected by a Battery Management System (BMS) or by other devices. In addition to the above-mentioned data, the collected performance parameter data may also include other performance parameters; this embodiment does not limit this. The battery can be a sodium-ion battery or other types of batteries. The collected data can be performance parameter data of individual cells or performance data of the battery module. The performance parameter data can be collected in real time during battery operation or at certain time intervals to enable real-time monitoring and evaluation of the battery's consistency during operation.

[0028] Step S102: Determine the time operator representing the battery state and usage based on performance parameter data. Specifically, during battery use, inconsistencies may not be significant initially, but these inconsistencies gradually become apparent over time, manifesting as large fluctuations in characteristics such as voltage or current. Therefore, this embodiment uses the calculation of the time operator to represent the current battery state and usage, that is, to determine the period in the battery's operation (this period represents any point in time from the first use to the end of the battery's lifespan) through the time operator. This time operator is then used for subsequent consistency evaluation, improving the accuracy of the evaluation results. It should be noted that the time operator can be defined and calculated according to actual application requirements. For example, it can be determined based on factors such as battery aging rate or temperature changes, or other factors, as long as it can represent the battery state and usage.

[0029] Step S103: Within a sliding window determined by the time operator, the sample entropy is calculated based on the performance parameter data. Specifically, sample entropy is a measure of time series complexity; the smaller the sample entropy, the smaller the time series complexity and the higher the self-similarity. In this embodiment, the calculated sample entropy reflects the complexity and uncertainty of the performance parameter data, and thus reflects the performance stability of the battery over a certain period. However, in related technologies, a fixed window size is usually used when calculating information entropy, which ignores the dynamic changes of the battery during use. Setting an excessively large window will reduce the sensitivity of entropy, making it difficult to detect inconsistent battery packs in the early stages; conversely, setting an excessively small window may lead to a high false alarm rate in the later stages. Therefore, in this embodiment, the sliding window is determined by using a time operator that characterizes the period in which the battery is located, so that the size of the sliding window includes the period in which the battery is located. The sample entropy obtained from this can also reflect the period in which the battery is located, thus making the consistency evaluation result based on sample entropy more accurate.

[0030] Step S104: The battery consistency is determined based on the relationship between sample entropy and a threshold determined by a time operator. Specifically, in related technologies, the threshold is usually set based on experience. However, similar to the setting of a sliding window, setting the threshold too high will reduce the sensitivity of entropy, while setting the threshold too low may result in a higher false alarm rate for the battery in later stages. In this embodiment, the threshold is determined by using a time operator that characterizes the period in which the battery is located, thereby enabling more accurate consistency assessment based on the threshold.

[0031] The battery consistency evaluation method provided in this invention collects battery performance parameter data in real time and determines a time operator accordingly. The time operator dynamically adjusts the sliding window size and threshold, thereby more accurately capturing dynamic changes in battery performance. This method can promptly identify inconsistent battery packs in the early stages of battery use, effectively avoiding misjudgments or omissions caused by improper fixed parameter settings. Furthermore, combining the time operator and sample entropy algorithm can more comprehensively reflect the true state of battery performance, especially when dealing with nonlinear and non-normally distributed data, where the evaluation results are more accurate and reliable. Moreover, compared to approximate entropy, sample entropy may better represent the entropy of the system. The advantage of this method is its ability to classify complex systems containing both fixed and random variables, achieving effective evaluation even with limited data points. In addition, the algorithm framework based on dynamic windows and sample entropy has broad applicability, suitable for various types of battery consistency evaluation, providing new ideas and methods for the field of battery consistency evaluation.

[0032] This embodiment provides a battery consistency evaluation method, which includes the following steps: Step S201: Obtain performance parameter data during battery operation; for details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0033] Step S202 involves cleaning the performance parameters to obtain cleaned data; filtering the cleaned data to obtain processed data; and standardizing the processed data to obtain standardized data. Specifically, the cleaning process removes outliers and erroneous data from the collected performance parameter data by setting reasonable thresholds or algorithms. The filtering process uses appropriate filtering algorithms (such as Kalman filtering, median filtering, etc.) to filter the data, thereby eliminating noise interference and improving data quality. Thus, cleaning the performance parameter data removes outliers and erroneous data; filtering the data eliminates noise interference; and standardizing the data converts data of different dimensions and ranges into a unified format, facilitating subsequent calculations.

[0034] In this embodiment, after obtaining the performance parameter data, the data is cleaned, filtered, and standardized, which effectively eliminates outliers, erroneous data, and noise interference, and improves the stability and reliability of the evaluation results.

[0035] Step S203: Determine the time operator characterizing battery state and usage based on performance parameter data; for details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0036] Step S204: Within the sliding window determined by the time operator, calculate the sample entropy based on the performance parameter data; for details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0037] Step S205: Determine the battery consistency based on the relationship between sample entropy and the threshold determined by the time operator. See details below. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0038] The battery consistency evaluation method provided in this embodiment of the invention This embodiment provides a battery consistency evaluation method, which includes the following steps: Step S301: Obtain performance parameter data during battery operation. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0039] Step S302: Determine the time operator characterizing the battery status and usage based on the performance parameter data.

[0040] Specifically, step S302 includes: Step S3021: Calculate the battery's state of health (SOH) based on performance parameters. Specifically, SOH reflects the battery's healthy lifespan and represents its charge, energy, and charge / discharge power. In this embodiment, the SOH is characterized by the ratio of the battery's current capacity to its nominal capacity, i.e., SOH = (Current Maximum Capacity / Initial Maximum Capacity). 100%.

[0041] Step S3022: Calculate the similarity of the charge-discharge curves based on performance parameters. Specifically, the charge-discharge curve is a curve representing the relationship between the battery charge-discharge current and the battery voltage, or the relationship between the electrode charge-discharge current and the electrode potential. In this embodiment, the similarity of the charge-discharge curves can be measured by calculating the similarity between the voltage or current at two time points in the curve. The similarity between the voltage or current can be cosine similarity or other similarities; this embodiment does not limit this. Therefore, the charge-discharge similarity can be expressed by the following formula: Similarity of charge and discharge curves

[0042] In the formula, and These represent the voltage or current at two different time points.

[0043] Step S3023: The time operator is calculated by weighting the health status and the similarity of the charge / discharge curves. Specifically, the time operator is calculated using the following formula:

[0044] In the formula, α and β are weighting coefficients, and SOH represents the health status. In practical applications, the weighting coefficients can be adjusted according to specific circumstances, such as battery model, application scenario, and performance requirements.

[0045] Step S303: Within the sliding window determined by the time operator, calculate the sample entropy based on the performance parameter data; Specifically, step S303 includes: Step S3031: Divide the performance parameter data into multiple subsequences according to the sliding window determined by the time operator; specifically, the sliding window can be calculated using the following formula: m=k1 / TO In the formula, TO represents the time operator, m represents the sliding window, and k1 is the scaling factor. It should be noted that since battery consistency is not significant in the early stages of use, it gradually becomes more pronounced over time. Therefore, the sliding window size is inversely proportional to the time operator; that is, the smaller the time operator, the larger the window to capture more performance parameter changes, and the larger the time operator, the smaller the window to reduce unnecessary computation. After determining the sliding window, the performance parameter data can be divided into multiple subsequences according to the time series. Since the performance parameter data may include voltage, current, temperature, and capacity, the subsequences can be divided separately for each performance parameter, i.e., separate subsequences for voltage, current, and temperature, resulting in multiple subsequences corresponding to voltage, current, and temperature.

[0046] Step S3032: The sample entropy of each subsequence is calculated using the sample entropy algorithm. Specifically, the sample entropy algorithm is implemented using the following process: Step a1, Sequence Segmentation: Arrange any subsequence from step S3031 into a vector sequence of dimension m according to its index. ( )=[ ( ), ( +1),…, ( + -1)], =1,2,…, +1 In the formula, m is the size of the sliding window, and these vectors represent m consecutive x values ​​starting from the i-th point.

[0047] Step a2, Define distance: Define vector ( )and ( The distance between each element is the Chebyshev distance, which is the maximum absolute value of the difference between the numerical values ​​of the elements. [ ( ), ( )] = [| ( + ) ( + )|], 0 1 Step a3, define similarity: For a given ,statistics ( )and ( The number of pairs of numbers whose distance is less than or equal to r, i.e., the approximate number. ( ): ( ) = count( [ ( ), ( )] r) In the formula, r is the similarity tolerance threshold. When the value of r is large, more information will be lost, while when the value of r is small, the statistical characteristics of the system cannot be estimated ideally. Typically, the value of r is between 0.1 and 0.25 std(x). ( The approximate quantity is the ratio of the approximate quantity to the total quantity, i.e., the approximate proportion. Therefore, the approximate proportion is calculated using the following formula: , =1,2,…, +1 Step a4, increase the dimension to m + 1 and recalculate the approximate proportion: , =1,2,…, +1 Step a5, therefore, the probability of matching between two time series with a window length of m and the probability of matching between two time series with a window length of m+1 are expressed by the following formulas:

[0048]

[0049] In the formula, This can be understood as the probability that two sequences match m points under a similarity tolerance of r, while This represents the probability that two sequences match at m+1 points.

[0050] Step a6, define sample entropy: the sample entropy SampEn of the signal sequence is:

[0051] For each subsequence obtained by the division, steps a1 to a6 above can be performed respectively to obtain the sample entropy of each subsequence.

[0052] Step S3033: Calculate the overall sample entropy of the battery based on the sample entropy of each subsequence. Specifically, after calculating the sample entropy of each subsequence, the overall sample entropy value can be calculated using methods such as weighted average or z-score. The overall sample entropy value comprehensively reflects the performance stability and consistency of the sodium-ion battery throughout the entire operation process.

[0053] Step S304: Determine the battery consistency based on the relationship between sample entropy and a threshold determined by the time operator. Specifically, the battery consistency can be graded based on sample entropy and a threshold determined by the time operator to obtain a consistency evaluation result. The threshold can be calculated using the following formula:

[0054] In the formula, TO represents the time operator, TH represents the threshold, and k2 is the scaling factor.

[0055] It should be noted that in the actual evaluation process, different scaling factors can be set, and multiple thresholds can be obtained by combining them with the time operator. Two adjacent thresholds constitute a consistency level. Therefore, the calculated comprehensive sample entropy of the battery can be compared with multiple thresholds to determine which two thresholds the comprehensive sample entropy falls between, thus determining the consistency level of the battery. In this embodiment, a higher consistency level indicates more stable battery performance and better consistency.

[0056] As a specific application embodiment of the present invention, such as Figure 2 As shown, taking sodium-ion batteries as an example, the consistency evaluation method for these batteries is explained: 1. Data collection.

[0057] The battery management system (BMS) or other data acquisition devices are used to collect real-time performance parameter data such as voltage, current, temperature, and capacity of the sodium-ion battery during operation. This data can be collected from individual cells or the entire battery module. The frequency and accuracy of data acquisition should be set according to the specific application requirements.

[0058] 2. Data preprocessing.

[0059] 2.1 The acquired data should be cleaned first. For example, by setting reasonable thresholds or algorithms, outliers and erroneous data in the collected performance parameter data can be removed.

[0060] 2.2 The cleaned data is then filtered. This embodiment employs appropriate filtering algorithms (such as Kalman filtering, median filtering, etc.) to filter the data, thereby eliminating noise interference and improving data quality.

[0061] 2.3. Standardize the filtered data. The standardization process converts the cleaned and filtered data into a uniform format to facilitate subsequent calculations and analysis.

[0062] 3. Time operator calculation.

[0063] The time operator is calculated based on the collected performance parameter data. The time operator can be defined according to the actual application requirements, such as a comprehensive indicator based on factors such as battery aging rate and temperature change.

[0064] 4. Dynamic window adjustment.

[0065] The size of the sliding window is dynamically adjusted based on the calculated time operator. When the time operator indicates a significant change in battery performance, the window size should be appropriately reduced to more accurately capture performance changes; conversely, when battery performance is stable, the window size can be appropriately increased to improve evaluation efficiency.

[0066] 5. Threshold setting.

[0067] The threshold is determined based on a time operator and a scaling factor. The scaling factor should be set taking into account factors such as different battery models, application scenarios, and performance requirements to ensure the accuracy and reliability of the evaluation results.

[0068] 6. Sample entropy calculation and consistency classification.

[0069] The sample entropy value of sodium-ion batteries is calculated using an adjusted sliding window and threshold. To calculate the sample entropy, the time series can be divided into subsequences, and then the sample entropy algorithm can be used to process each subsequence to obtain its sample entropy. Finally, the comprehensive sample entropy value is calculated based on the sample entropy of each subsequence.

[0070] When performing threshold grading, sodium-ion batteries can be graded based on the total sample entropy value. A lower consistency level indicates inconsistencies within the battery pack, while a higher consistency level indicates better consistency within the battery pack. The specific grading results can be output as visual charts or reports for user reference and analysis.

[0071] This embodiment also provides a battery consistency evaluation device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. 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.

[0072] This embodiment provides a battery consistency evaluation device, such as... Figure 3 As shown, it includes: Data acquisition module 31 is used to acquire performance parameter data during battery operation; The time operator determination module 32 is used to determine the time operator that characterizes the battery status and usage based on the performance parameter data; The sample entropy calculation module 33 is used to calculate the sample entropy based on performance parameter data within a sliding window determined by the time operator; Evaluation module 34 is used to determine the consistency of the battery based on the relationship between the sample entropy and the threshold determined by the time operator.

[0073] In one optional implementation, the time operator determination module is specifically used to: calculate the battery's health state based on performance parameters; calculate the charge-discharge curve similarity based on performance parameters; and calculate the time operator by weighting the health state and the charge-discharge curve similarity.

[0074] In one alternative implementation, the time operator is calculated using the following formula:

[0075] In the formula, α and β are weighting coefficients, and SOH represents the health status.

[0076] In one alternative implementation, the sliding window and the threshold are calculated using the following formulas: m=k1 / TO

[0077] In the formula, TO represents the time operator, m represents the sliding window, TH represents the threshold, and k1 and k2 are scaling coefficients.

[0078] In one alternative implementation, the sample entropy calculation module is specifically used to: divide the performance parameter data into multiple subsequences according to a sliding window determined by the time operator; calculate the sample entropy of each subsequence using a sample entropy algorithm; and calculate the overall sample entropy of the battery based on the sample entropy of each subsequence.

[0079] In one alternative implementation, the evaluation module is specifically used to: classify the battery for consistency based on the sample entropy and a threshold determined by the time operator, thereby obtaining a consistency evaluation result.

[0080] In one optional embodiment, the apparatus further includes: a preprocessing module, specifically used to clean the performance parameters to obtain cleaned data; to filter the cleaned data to obtain processed data; and to standardize the processed data to obtain standardized data.

[0081] Further functional descriptions of the above modules are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0082] This invention also provides a computer device having the above-described features. Figure 3 The battery consistency evaluation device shown.

[0083] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0084] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0085] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0086] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0087] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0088] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0089] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0090] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0091] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A battery consistency evaluation method, characterized in that, The method includes: Acquire performance parameter data during battery operation; Determine time operators characterizing battery status and usage based on the performance parameter data, including: Calculate the battery's health status based on the performance parameter data; Calculate the similarity of the charge-discharge curves based on the performance parameter data; A time operator is calculated by weighting the health status and the similarity of the charge / discharge curves; the time operator is calculated using the following formula: In the formula, α and β are weighting coefficients, and SOH represents the health status; within the sliding window determined by the time operator, the sample entropy is calculated based on the performance parameter data; The consistency of the battery is determined based on the relationship between the sample entropy and the threshold determined by the time operator; the sliding window and the threshold are calculated using the following formulas: In the formula, TO represents the time operator, m represents the sliding window, TH represents the threshold, and k1 and k2 are proportional coefficients.

2. The method according to claim 1, characterized in that, Within a sliding window determined by the time operator, the sample entropy is calculated based on the performance parameter data, including: The performance parameter data is divided into multiple subsequences based on a sliding window determined by the time operator; The sample entropy of each subsequence is calculated using the sample entropy algorithm; The overall sample entropy of the battery is calculated based on the sample entropy of each subsequence.

3. The method according to claim 1, characterized in that, Determining battery consistency based on the relationship between the sample entropy and the threshold determined by the time operator includes: The batteries are graded for consistency based on the sample entropy and the threshold determined by the time operator, and the consistency evaluation results are obtained.

4. The method according to claim 1, characterized in that, After acquiring the performance parameter data during battery operation, the method further includes: The performance parameter data is cleaned to obtain cleaned data; The cleaned data is filtered to obtain the processed data. The processed data is standardized to obtain standardized data.

5. A battery consistency evaluation device, characterized in that, The device includes: The data acquisition module is used to acquire performance parameter data during battery operation. The time operator determination module is used to determine time operators characterizing battery state and usage based on the performance parameter data, including: Calculate the battery's health status based on the performance parameter data; Calculate the similarity of the charge-discharge curves based on the performance parameter data; A time operator is calculated by weighting the health status and the similarity of the charge / discharge curves; the time operator is calculated using the following formula: In the formula, α and β are weighting coefficients, and SOH represents the health status; The sample entropy calculation module is used to calculate the sample entropy based on the performance parameter data within a sliding window determined by the time operator. The evaluation module is used to determine the battery consistency based on the relationship between the sample entropy and a threshold determined by the time operator; the sliding window and the threshold are calculated using the following formulas: In the formula, TO represents the time operator, m represents the sliding window, TH represents the threshold, and k1 and k2 are proportional coefficients.

6. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the battery consistency evaluation method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the battery conformity assessment method according to any one of claims 1 to 4.

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