Lithium battery process parameter optimization method and device, electronic equipment and storage medium

Abstract

The invention discloses a lithium battery process parameter optimization method and device, electronic equipment and a storage medium, and relates to the technical field of lithium batteries. The method comprises the following steps: acquiring current process parameter data of the lithium battery in a preset time period, and judging whether the current process parameter data reaches a stable working condition or not according to a preset variance threshold value; if the current process parameter data reach the stable working condition, clustering the current process parameter data to obtain a current process data category corresponding to the current process parameter data; calculating a current performance index according to the current process parameter data; and obtaining a historical performance index corresponding to the current process data category, and adjusting the current process parameter data according to the current performance index and the historical performance index. Therefore, adjustment of the current process parameters is guided according to the historical performance indexes and the current performance indexes obtained through calculation based on the current process parameter data, errors caused by artificial experience are reduced, and the output quality and consistency of the cell production process are improved.

Description

Technical Field

[0001] The present invention relates to the field of lithium battery technology, and in particular to a lithium battery process parameter optimization method, device, electronic equipment and storage medium. Background Art

[0002] At present, the control of equipment parameters in the production process of lithium battery cells mainly depends on the experience of operators. However, the differences in control strategies and strengths among different operators have led to the inability to fully guarantee the consistency of output quality in the lithium battery production process. Summary of the invention

[0003] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a lithium battery process parameter optimization method, device, electronic device and storage medium, which are used to obtain the current performance indicators corresponding to the current process parameter data through a data mining algorithm, and guide the regulation of the current process parameters according to the current performance indicators and the historical performance indicators in the historical data, so that the output quality of the lithium battery cell production process is better and the consistency is higher.

[0004] The present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a method for optimizing lithium battery process parameters, comprising:

[0006] Acquire current process parameter data of the lithium battery within a preset time period, and determine whether the current process parameter data reaches a stable operating condition according to a preset variance threshold;

[0007] If the current process parameter data reaches a stable working condition, clustering the current process parameter data to obtain a current process data category corresponding to the current process parameter data;

[0008] Calculate the current performance index according to the current process parameter data;

[0009] Acquire the historical performance index corresponding to the current process data category, and adjust the current process parameter data according to the current performance index and the historical performance index.

[0010] In one embodiment, the calculating the current performance index according to the current process parameter data includes:

[0011] Calculate the difference between the current mean value corresponding to the current process parameter data and the preset lithium battery capacity parameter value;

[0012] The current performance index is calculated according to a preset combination coefficient, a current variance corresponding to the current process parameter data, and the difference.

[0013] In one embodiment, judging whether the current process parameter data reaches a stable working condition according to a preset variance threshold value includes:

[0014] Determine whether the current variance corresponding to the current process parameter data is less than the preset variance threshold;

[0015] If the current variance is less than the preset variance threshold, the current process parameter data reaches a stable working condition;

[0016] If the current variance is greater than or equal to the preset variance threshold, the current process parameter data has not reached a stable operating condition.

[0017] In one embodiment, after judging whether the current process parameter data reaches a stable working condition according to a preset variance threshold, the method includes:

[0018] If the current process parameter data does not reach a stable operating condition, the step of obtaining the current process parameter data of the lithium battery within a preset time period is performed again.

[0019] In one embodiment, the current process parameter data includes current external constraint parameter data, and clustering the current process parameter data to obtain the current process data category corresponding to the current process parameter data includes:

[0020] The current external constraint parameter data is clustered to obtain the current process data category.

[0021] In one embodiment, the current process parameter data includes current operation parameter data, and adjusting the current process parameter data according to the current performance indicator and the historical performance indicator includes:

[0022] Determining whether the current performance indicator is less than the historical performance indicator;

[0023] If the current performance indicator is greater than or equal to the historical performance indicator, the current operating parameter data is adjusted according to the historical performance indicator.

[0024] In one embodiment, after determining whether the current performance indicator is less than the historical performance indicator, the method further includes:

[0025] If the current performance index is less than the historical performance index, the current operating parameter data remains unchanged, and the current performance index is used as the historical performance index corresponding to the current process data category.

[0026] In a second aspect, the present invention provides a lithium battery process parameter optimization device, comprising:

[0027] A judgment module, used to obtain current process parameter data of the lithium battery within a preset time period, and judge whether the current process parameter data reaches a stable working condition according to a preset variance threshold;

[0028] A clustering module, used for clustering the current process parameter data to obtain a current process data category corresponding to the current process parameter data if the current process parameter data reaches a stable working condition;

[0029] A calculation module, used for calculating the current performance index according to the current process parameter data;

[0030] The adjustment module is used to obtain the historical performance index corresponding to the current process data category, and adjust the current process parameter data according to the current performance index and the historical performance index.

[0031] In a third aspect, the present invention proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for optimizing lithium battery process parameters as described in the first aspect is implemented.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the lithium battery process parameter optimization method as described in the first aspect.

[0033] The lithium battery process parameter optimization method, device, electronic device and storage medium disclosed in the present invention obtain the current process parameter data of the lithium battery within a preset time period, and judge whether the current process parameter data reaches a stable working condition according to a preset variance threshold; if the current process parameter data reaches a stable working condition, cluster the current process parameter data to obtain the current process data category corresponding to the current process parameter data; calculate the current performance index according to the current process parameter data; obtain the historical performance index corresponding to the current process data category, and adjust the current process parameter data according to the current performance index and the historical performance index. In this way, it is judged whether the stable working condition is reached according to the current process parameter data, and the current process data category is identified under the stable working condition, and the historical performance index corresponding to the current process data category is further obtained from the accumulated historical data, and the adjustment of the current process parameters is guided according to the historical performance index and the current performance index calculated based on the current process parameter data, so as to reduce the error caused by manual experience and improve the output quality and consistency of the battery cell production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope of protection of the present invention. In each of the drawings, similar components are numbered similarly.

[0035] Figure 1 A schematic diagram of a process flow of the lithium battery process parameter optimization method proposed in this embodiment is shown;

[0036] Figure 2 Another schematic diagram of the process of optimizing the lithium battery process parameters proposed in this embodiment is shown;

[0037] Figure 3 Another schematic diagram of the process of optimizing the lithium battery process parameters proposed in this embodiment is shown;

[0038] Figure 4 A schematic diagram of the structure of the lithium battery process parameter optimization device proposed in this embodiment is shown.

[0039] Description of the accompanying drawings:

[0040] 400 - lithium battery process parameter optimization device; 401 - judgment module; 402 - clustering module; 403 - calculation module; 404 - adjustment module. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0042] The components of the embodiments of the present invention generally described and shown in the drawings herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0043] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present invention, are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0044] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0045] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present invention belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present invention.

[0046] Example 1

[0047] The production process of lithium battery cells includes multiple steps, such as slurrying, coating, rolling and winding. The output quality of each process depends not only on the regulation of process parameters, but also on the output quality of the previous process. Therefore, when the input quality fluctuates or the environmental conditions change, the process parameters need to be adjusted in time to ensure that the output quality reaches a better level.

[0048] The disclosed embodiment provides a lithium battery process parameter optimization method for obtaining current performance indicators corresponding to current process parameter data through a data mining algorithm, and guiding the regulation of current process parameters according to the current performance indicators and historical performance indicators in historical data, so that the output quality of the lithium battery cell production process is better and more consistent.

[0049] See also Figure 1 A lithium battery process parameter optimization method includes steps S101 to S104, and each step is described in detail below.

[0050] Step S101, obtaining current process parameter data of the lithium battery within a preset time period, and judging whether the current process parameter data reaches a stable working condition according to a preset variance threshold.

[0051] In this embodiment, since the current process parameter data obtained contains unstable data caused by various debugging processes, only the process parameters during stable operation can reflect the actual situation of the current process of the lithium battery, so it is necessary to identify the stable working condition. Variance is an important indicator to measure the degree of data dispersion. The smaller the variance, the more stable the data. Therefore, the current process parameter data of the lithium battery within a preset time period is obtained, and the current process parameter data is judged according to the preset variance threshold whether it reaches a stable working condition. Stable working conditions refer to the variance of each parameter in the current process parameters during operation within the prescribed preset variance threshold.

[0052] Among them, the relevant parameter data of the coating stage of battery cell production is taken as the research object. The current process parameters include slurry viscosity, slurry solid content, temperature, intermittent coating die gap value, intermittent liquid supply pump speed value, reflux pressure and thickness (surface density) average, etc.

[0053] It should be noted that due to the different data collection locations and sampling frequencies, the current process parameter data needs to be aligned according to time. Assume that the time t of parameter 1 detection is 1 , after Δt 1 Arrival parameter 2 detection time t 2 , and then after Δt 2 Arrival parameter 3 detection time t 3 Therefore, t 1 The parameters 1 and t at the moment 2 The parameters 2 and t at the moment 3 The parameters 3 at the moment constitute a set of associated current process parameter data, and the current process parameter data after the associated alignment is judged to be stable. It should be noted that due to the different sampling frequencies of the three, interpolation or resampling operations are required so that there are data in the corresponding time periods of the three.

[0054] For example, taking the coating stage as an example, among the current process parameters, the gap value of the gap coating die head, the intermittent liquid supply pump speed value and the reflux pressure are derived from the coating die head, the temperature is derived from the mold temperature controller, the slurry viscosity and solid content are derived from the input slurry, and the surface density mean (data of multiple surface density points are collected at the same time, and the mean is used to represent the surface density at a certain moment) is derived from the thickness gauge. The slurry data is sampled and tested based on the barrel. The slurry distribution in the barrel is uniform by default, so the sampled slurry data is considered to be able to reflect the status of all slurries in the current barrel. The time granularity of the slurry is the largest, at the hour level, while the time granularity of the equipment parameters and thickness can reach the second level. In addition, there is a time difference from the spraying of the equipment to the thickness detection. The equipment parameters at time t1 should correspond to the thickness data at time t1+Δt, where Δt is the time difference. Taking the sampling time of thickness as the standard, the slurry data needs to be interpolated and expanded due to the larger time granularity, and the equipment parameter data needs to be sampled less due to the smaller time granularity.

[0055] It should be noted that the equipment collects the current process parameter data in real time, and determines whether the current process parameter data within the current time period has reached a stable operating condition every preset time period, and makes subsequent adjustments to the process parameter data, thereby achieving timely adjustment of the process parameters based on whether the current operating condition of the lithium battery is stable, thereby meeting the production quality requirements of the lithium battery.

[0056] In a specific embodiment, see Figure 2, step S101 includes steps S1011 to S1013, and each step is described in detail below.

[0057] Step S1011, determining whether the current variance corresponding to the current process parameter data is less than the preset variance threshold.

[0058] In this embodiment, the current variance of the current process parameter data is calculated, and it is determined whether the current variance is less than a preset variance threshold. Variance is a statistic that measures the degree of data dispersion. The smaller the variance, the more concentrated and stable the data. By comparing with the preset variance threshold, it can be determined whether the current process parameter data meets the stability requirement.

[0059] Step S1012: If the current variance is less than the preset variance threshold, the current process parameter data reaches a stable working condition.

[0060] In this embodiment, if the current variance is less than the preset variance threshold, the current process parameter data reaches a stable state. Exemplarily, the current variance of the current process parameter data is less than the preset variance threshold (eg, 0.05) within a preset time period (eg, 20 minutes).

[0061] It should be noted that the current process parameter data includes multiple parameter data, and the current process parameter data can only reach a stable working condition when the variance of each parameter data is less than a preset variance threshold.

[0062] Step S1013: if the current variance is greater than or equal to the preset variance threshold, the current process parameter data has not reached a stable operating condition.

[0063] In this embodiment, if the current variance is greater than or equal to the preset variance threshold, the current process parameter data has not reached a stable operating condition. Exemplarily, the current variance of the current process parameter data is greater than or equal to the preset variance threshold (e.g., 0.05) within a preset time period (e.g., 20 minutes).

[0064] In a specific embodiment, after step S101, the method further includes: if the current process parameter data does not reach a stable operating condition, re-executing the step of obtaining the current process parameter data of the lithium battery within a preset time period.

[0065] In this embodiment, if the current process parameter data does not reach a stable working condition, the process goes to step S101 to re-execute the step of obtaining the current process parameter data of the lithium battery within a preset time period. Exemplarily, if the current process parameter data within 20 minutes does not reach a stable working condition, the parameter is recursively extrapolated to 5 minutes of data to form 20 minutes, and the data of 20 minutes is taken to determine whether the process parameter has entered a stable working condition again.

[0066] Step S102: If the current process parameter data reaches a stable working condition, clustering is performed on the current process parameter data to obtain a current process data category corresponding to the current process parameter data.

[0067] In this embodiment, if the current process parameter data reaches a stable working condition, the current process parameter data can be clustered using a clustering method to obtain the current process data category corresponding to the current process parameter data. Among them, the clustering method can be k-means clustering, which is not limited here. Clustering can divide a group of objects into several categories (or clusters) according to their characteristics or attributes, so that the objects in the same category have a higher similarity, while the objects in different categories have a lower similarity. Compared with artificial interval segment division, the result of clustering division is closer to the actual situation.

[0068] In a specific embodiment, the current process parameter data includes current external constraint parameter data, and step S102 includes: clustering the current external constraint parameter data to obtain the current process data category.

[0069] In this embodiment, the current external constraint parameter data in the current process parameter data is clustered to obtain the current process data category. The same current process data category has the same current external constraint parameter data. It should be noted that each external constraint parameter needs to be clustered. Among them, in the coating stage, the current external constraint parameters may include the solid content, viscosity, and temperature of the slurry. The current process data category corresponding to the solid content of the slurry is set to 7 categories, the current process data category corresponding to the viscosity of the slurry is set to 7 categories, and the current process data category corresponding to the temperature is set to 5 categories. The specific number of current process data categories comes from the determination of quality and process personnel.

[0070] Step S103, calculating the current performance index according to the current process parameter data.

[0071] In this embodiment, as external constraints and time change, the current process parameter data needs to be dynamically adjusted so that the current process output is better under the current constraint parameters. First, the current performance index needs to be calculated based on the current process parameter data.

[0072] In a specific embodiment, see Figure 3 Step S103 includes steps S1031 to S1032, and each step is described in detail below.

[0073] Step S1031, calculating the difference between the current mean value corresponding to the current process parameter data and the preset lithium battery capacity parameter value.

[0074] In this embodiment, the current mean value corresponding to the current process parameter data is calculated and the preset lithium battery capacity parameter value y p The difference between the values ​​of y and y is shown in Table 1. p It can be a preset surface density value, which is not limited here.

[0075] Step S1032, calculating the current performance index according to the preset combination coefficient, the current variance corresponding to the current process parameter data, and the difference.

[0076] In this embodiment, the current performance index γ is calculated according to the preset combination coefficient, the current variance σ corresponding to the current process parameter data, and the difference. Specifically, the calculation formula of the current performance index γ can be: Wherein, α and β are preset combination coefficients (eg, α=0.8 and β=0.2).

[0077] Step S104, obtaining the historical performance index corresponding to the current process data category, and adjusting the current process parameter data according to the current performance index and the historical performance index.

[0078] In this embodiment, the historical performance index corresponding to the current process data category is obtained from the historical database, and the current process parameter data is adjusted according to the current performance index and the historical performance index, so that the current process output is better. At the same time, by processing the data of the current process parameter data, the adjustment and optimization of the current process parameter data according to the performance index under stable working conditions is realized, so that this data processing method is used to replace the manual operation of the operator, avoiding the inconsistency of output quality caused by the differences in control strategies and strength of different operators. Among them, the historical performance index is the best historical performance index corresponding to the current process data category.

[0079] In a specific embodiment, step S104 includes steps S1041 to S1042, and each step is described in detail below.

[0080] Step S1041, determining whether the current performance index is smaller than the historical performance index.

[0081] In this embodiment, according to the calculation formula It can be seen that if the current mean and the preset lithium battery capacity parameter value (preset surface density value)y p The smaller the difference, the closer the current output quality is to the set value. If the current variance σ is smaller, the more stable the current data is. When both the difference and the current variance σ are smaller, the current performance index is also smaller, that is, the smaller the performance index is, the better the output quality is during production. Therefore, it is necessary to determine whether the current performance index is smaller than the historical performance index.

[0082] Step S1042: If the current performance index is greater than or equal to the historical performance index, the current operating parameter data is adjusted according to the historical performance index.

[0083] In this embodiment, if the current performance index is greater than or equal to the historical performance index, it can be known that the historical performance index is better than the current performance index, and the current operating parameter data in the current process parameter data needs to be adjusted according to the historical performance index to make the output quality of the production process better. Specifically, the current operating parameter data is gradually adjusted to the operating parameter data corresponding to the historical performance index.

[0084] In a specific embodiment, after step S1041, it also includes: if the current performance indicator is less than the historical performance indicator, the current operating parameter data remains unchanged, and the current performance indicator is used as the historical performance indicator corresponding to the current process data category.

[0085] In this embodiment, if the current performance indicator is smaller than the historical performance indicator, it can be known that the current performance indicator is better than the historical performance indicator. Therefore, the current operating parameter data remains unchanged, and the current performance indicator is stored in the historical database as the best performance indicator corresponding to the current process data category. At the same time, the current operating parameter data is stored, so that the next time the historical performance indicator corresponding to the current process data category is obtained, the current performance indicator is used as the historical performance indicator.

[0086] It should be noted that the above method of finding the best process parameter optimization method based on stable working conditions using a dynamic recursive method is simple and universal, can be used in various processes of battery cell production, and is suitable for situations where there is a lot of historical data.

[0087] The lithium battery process parameter optimization method proposed in this embodiment obtains the current process parameter data of the lithium battery within a preset time period, and determines whether the current process parameter data reaches a stable working condition according to a preset variance threshold; if the current process parameter data reaches a stable working condition, cluster the current process parameter data to obtain the current process data category corresponding to the current process parameter data; calculate the current performance index according to the current process parameter data; obtain the historical performance index corresponding to the current process data category, and adjust the current process parameter data according to the current performance index and the historical performance index. In this way, it is determined whether the stable working condition is reached according to the current process parameter data, and the current process data category is identified under the stable working condition, and the historical performance index corresponding to the current process data category is further obtained from the accumulated historical data, and the adjustment of the current process parameters is guided according to the historical performance index and the current performance index calculated based on the current process parameter data, so as to reduce the error caused by manual experience and improve the output quality and consistency of the battery cell production process.

[0088] Example 2

[0089] In addition, the present disclosure provides a lithium battery process parameter optimization device 400, see Figure 4 , the device comprises:

[0090] The judgment module 401 is used to obtain the current process parameter data of the lithium battery within a preset time period, and judge whether the current process parameter data reaches a stable working condition according to a preset variance threshold;

[0091] A clustering module 402 is used to cluster the current process parameter data to obtain a current process data category corresponding to the current process parameter data if the current process parameter data reaches a stable working condition;

[0092] A calculation module 403, used to calculate the current performance index according to the current process parameter data;

[0093] The adjustment module 404 is used to obtain the historical performance index corresponding to the current process data category, and adjust the current process parameter data according to the current performance index and the historical performance index.

[0094] Optionally, the calculation module 403 is also used to calculate the difference between the current mean value corresponding to the current process parameter data and the preset lithium battery capacity parameter value; and calculate the current performance index according to the preset combination coefficient, the current variance corresponding to the current process parameter data and the difference.

[0095] Optionally, the judgment module 401 is also used to judge whether the current variance corresponding to the current process parameter data is less than the preset variance threshold; if the current variance is less than the preset variance threshold, the current process parameter data reaches a stable operating condition; if the current variance is greater than or equal to the preset variance threshold, the current process parameter data does not reach a stable operating condition.

[0096] Optionally, the judgment module 401 is further configured to re-execute the step of obtaining the current process parameter data of the lithium battery within a preset time period if the current process parameter data has not reached a stable operating condition.

[0097] Optionally, the current process parameter data includes current external constraint parameter data, and the clustering module 402 is further configured to cluster the current external constraint parameter data to obtain the current process data category.

[0098] Optionally, the current process parameter data includes current operating parameter data, and the adjustment module 404 is also used to determine whether the current performance indicator is less than the historical performance indicator; if the current performance indicator is greater than or equal to the historical performance indicator, the current operating parameter data is adjusted according to the historical performance indicator.

[0099] Optionally, the adjustment module 404 is further configured to, if the current performance index is smaller than the historical performance index, keep the current operating parameter data unchanged, and use the current performance index as the historical performance index corresponding to the current process data category.

[0100] The device provided in the embodiment of the present disclosure can execute the steps of the lithium battery process parameter optimization method provided in Example 1, which will not be described again to avoid repetition.

[0101] The lithium battery process parameter optimization device proposed in this embodiment obtains the current process parameter data of the lithium battery within a preset time period, and determines whether the current process parameter data reaches a stable working condition according to a preset variance threshold; if the current process parameter data reaches a stable working condition, cluster the current process parameter data to obtain the current process data category corresponding to the current process parameter data; calculate the current performance index according to the current process parameter data; obtain the historical performance index corresponding to the current process data category, and adjust the current process parameter data according to the current performance index and the historical performance index. In this way, it is determined whether a stable working condition is reached according to the current process parameter data, and the current process data category is identified under the stable working condition, and the historical performance index corresponding to the current process data category is further obtained from the accumulated historical data, and the adjustment of the current process parameters is guided according to the historical performance index and the current performance index calculated based on the current process parameter data, so as to reduce the error caused by manual experience and improve the output quality and consistency of the battery cell production process.

[0102] Example 3

[0103] In addition, an embodiment of the present disclosure provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the lithium battery process parameter optimization method described in Example 1 is implemented.

[0104] The device provided in the embodiment of the present disclosure can execute the steps of the lithium battery process parameter optimization method provided in Example 1, which will not be described again to avoid repetition.

[0105] Example 4

[0106] The embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the lithium battery process parameter optimization method described in Embodiment 1 is implemented.

[0107] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0108] The computer-readable storage medium provided in this embodiment can implement the lithium battery process parameter optimization method provided in Example 1, and will not be described again here to avoid repetition.

[0109] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limiting, and thus other examples of the exemplary embodiments may have different values.

[0110] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0111] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A lithium battery process parameter optimization method, characterized in that: include: Acquire current process parameter data of the lithium battery within a preset time period, and determine whether the current process parameter data reaches a stable operating condition according to a preset variance threshold; If the current process parameter data reaches a stable working condition, clustering the current process parameter data to obtain a current process data category corresponding to the current process parameter data; Calculate the current performance index according to the current process parameter data; Acquire the historical performance index corresponding to the current process data category, and adjust the current process parameter data according to the current performance index and the historical performance index.

2. The lithium battery process parameter optimization method according to claim 1, characterized in that: The calculating the current performance index according to the current process parameter data includes: Calculate the difference between the current mean value corresponding to the current process parameter data and the preset lithium battery capacity parameter value; The current performance index is calculated according to a preset combination coefficient, a current variance corresponding to the current process parameter data, and the difference.

3. The lithium battery process parameter optimization method according to claim 1, characterized in that: The determining whether the current process parameter data reaches a stable working condition according to a preset variance threshold comprises: Determine whether the current variance corresponding to the current process parameter data is less than the preset variance threshold; If the current variance is less than the preset variance threshold, the current process parameter data reaches a stable working condition; If the current variance is greater than or equal to the preset variance threshold, the current process parameter data has not reached a stable operating condition.

4. The lithium battery process parameter optimization method according to claim 1, characterized in that: After judging whether the current process parameter data reaches a stable working condition according to the preset variance threshold, the method includes: If the current process parameter data does not reach a stable operating condition, the step of obtaining the current process parameter data of the lithium battery within a preset time period is performed again.

5. The lithium battery process parameter optimization method according to claim 1, characterized in that: The current process parameter data includes current external constraint parameter data, and clustering the current process parameter data to obtain the current process data category corresponding to the current process parameter data includes: The current external constraint parameter data is clustered to obtain the current process data category.

6. The lithium battery process parameter optimization method according to claim 1, characterized in that: The current process parameter data includes current operation parameter data, and the adjusting the current process parameter data according to the current performance index and the historical performance index includes: Determining whether the current performance indicator is less than the historical performance indicator; If the current performance indicator is greater than or equal to the historical performance indicator, the current operating parameter data is adjusted according to the historical performance indicator.

7. The lithium battery process parameter optimization method according to claim 6, characterized in that: After determining whether the current performance index is less than the historical performance index, the method further includes: If the current performance index is less than the historical performance index, the current operating parameter data remains unchanged, and the current performance index is used as the historical performance index corresponding to the current process data category.

8. A lithium battery process parameter optimization device, characterized in that: include: A judgment module, used to obtain current process parameter data of the lithium battery within a preset time period, and judge whether the current process parameter data reaches a stable working condition according to a preset variance threshold; A clustering module, used for clustering the current process parameter data to obtain a current process data category corresponding to the current process parameter data if the current process parameter data reaches a stable working condition; A calculation module, used for calculating the current performance index according to the current process parameter data; The adjustment module is used to obtain the historical performance index corresponding to the current process data category, and adjust the current process parameter data according to the current performance index and the historical performance index.

9. An electronic device, characterized in that: It comprises a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the lithium battery process parameter optimization method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the lithium battery process parameter optimization method as described in any one of claims 1 to 7.

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