Battery workshop temperature management method, device and equipment and readable storage medium

Abstract

A battery workshop temperature management method, device and equipment and readable storage medium, relate to the technical field of battery production, including the target temperature sensing module is arranged in the initial position to be measured in the formation process workshop;Obtain the first temperature series corresponding to the temperature collected by the target temperature sensing module within a predetermined time period, and the second temperature series corresponding to the temperature collected by the preset temperature sensing module arranged in the storage location within a predetermined time period;Correlation fitting is carried out on the first temperature series and the second temperature series, and whether the initial position to be measured is determined as the best arrangement position of the target temperature sensing module based on the correlation fitting result;The real-time temperature collected by the target temperature sensing module at the best arrangement position is taken as the real-time temperature of the formation process workshop, so that the actual battery real temperature in the actual storage location can be accurately characterized.

Description

Technical Field

[0001] This application relates to the field of battery manufacturing technology, specifically to a method, apparatus, equipment, and readable storage medium for temperature control in a battery workshop. Background Technology

[0002] In battery production, the formation stage is a critical step. To improve productivity and formation reaction efficiency, most formation processes employ high-temperature formation (e.g., 35℃~60℃). The storage temperature of the battery during formation directly affects the SEI film formation performance, and temperature fluctuations can impact the consistency of process data (such as formation voltage and electrolyte loss), affecting product quality control. Currently, negative pressure formation is commonly used. Because gas is generated internally during formation, it needs to be extracted from the electrolyte injection port under negative pressure. However, the boiling point of the electrolyte decreases under negative pressure, and its volatility is significantly affected by temperature. A directly related factor is the electrolyte loss during formation, which directly relates to material utilization and process cost. Data shows that cooling within design specifications, such as a 2-3℃ reduction in storage temperature, can decrease overall electrolyte loss by 10%~20% without affecting battery performance. Therefore, accurate temperature control in the formation process workshop is crucial.

[0003] In related technologies, temperature control in the battery industry primarily relies on randomly deployed single-point temperature probes for data acquisition. This method works well for temperature-insensitive workshops without cabinet-style workstations (such as front-end manufacturing, mid-stage assembly, and electrolyte injection). However, it has a significant impact on temperature-sensitive workshops with cabinet-style workstations. For example, in the formation process, where electrolyte loss is directly related to temperature, the battery temperature inside the storage area is often higher than the temperature outside due to the heat generated by the batteries themselves during the formation process. However, the single-point temperature probes are randomly deployed within the formation workshop, failing to consider the specific working conditions of the formation process. Consequently, the collected temperatures cannot accurately represent the actual temperature of the batteries inside the storage area, and this discrepancy increases the pressure on plant heating, energy consumption, and electrolyte waste. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and readable storage medium for temperature control in a battery workshop, which can solve the technical problem in the prior art that the temperature probes are randomly deployed in the formation process workshop, resulting in the inability to accurately characterize the actual temperature of the batteries in the actual storage location.

[0005] In a first aspect, embodiments of this application provide a method for temperature control in a battery workshop, comprising the following steps: The target temperature sensing module is deployed at the initial position to be measured within the chemical formation process workshop; Acquire the first temperature sequence corresponding to the temperature collected by the target temperature sensing module within a preset time period, and the second temperature sequence corresponding to the temperature collected by the preset temperature sensing module deployed in the storage location within a preset time period. The first temperature series and the second temperature series are subjected to correlation fitting, and the correlation fitting results are used to determine whether the initial position to be measured is the optimal placement position of the target temperature sensing module. The real-time temperature collected by the target temperature sensing module at the optimal deployment location is used as the real-time temperature of the chemical formation process workshop.

[0006] In conjunction with the first aspect, in one implementation, the step of performing correlation fitting on the first temperature series and the second temperature series, and determining whether to use the initial position to be measured as the optimal placement position of the target temperature sensing module based on the fitting result, includes: The correlation coefficient is obtained by performing correlation fitting on the first temperature series and the second temperature series; If the correlation coefficient is greater than the preset correlation coefficient threshold, then the initial position to be measured is taken as the optimal placement position of the target temperature sensing module. If the correlation coefficient is less than or equal to the correlation coefficient threshold, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and the initial position to be measured for placing the target temperature sensing module will be re-determined.

[0007] In conjunction with the first aspect, in one embodiment, the number of target temperature sensing modules is multiple, and the step of performing correlation fitting on the first temperature series and the second temperature series, and determining whether to use the initial position to be measured as the optimal placement position of the target temperature sensing modules based on the fitting result, includes: For each target temperature sensing module, the first and second temperature series corresponding to it are fitted to obtain the correlation coefficient; Sort all correlation coefficients in descending order and determine whether the target correlation coefficient at the top is greater than the correlation coefficient threshold. If so, the initial location of the target temperature sensing module corresponding to the target correlation coefficient is taken as the optimal deployment location. If not, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and a new initial position to be measured for placing the target temperature sensing module will be determined.

[0008] In conjunction with the first aspect, in one implementation, the fitting method is linear fitting or nonlinear parametric regression.

[0009] In conjunction with the first aspect, in one embodiment, prior to the step of deploying the target temperature sensing module at the initial location to be measured within the formation process workshop, the method further includes: The target deployment space is determined based on the airflow information of the formation cabinet's location in the formation process workshop and the height of the formation cabinet. The initial position of the target temperature sensing module within the target deployment space is determined based on the preset measurable spatial range of the target temperature sensing module and the target deployment space.

[0010] In conjunction with the first aspect, in one embodiment, if the optimal placement position of the target temperature sensing module is the initial position to be measured, the method further includes: The target sequence is obtained by subtracting the first temperature sequence and the second temperature sequence; Construct a normal probability graph based on the target sequence; Target data is selected from the normal probability graph by pre-setting confidence intervals, and compensation values ​​are determined based on the target data; The compensation value is used to compensate for the real-time temperature collected by the target temperature sensing module at the optimal deployment location.

[0011] In conjunction with the first aspect, in one embodiment, after the step of determining the compensation value based on the target data, the method further includes: Acquire real-time environmental parameters, including humidity and airflow velocity; A new compensation value is determined based on the real-time environmental parameters and the compensation value, and the step of compensating the real-time temperature collected by the target temperature sensing module at the optimal deployment position based on the compensation value is performed based on the new compensation value.

[0012] Secondly, embodiments of this application provide a battery workshop temperature control device, comprising: The deployment module is used to deploy the target temperature sensing module at the initial position to be measured in the chemical formation process workshop. The acquisition module is used to acquire the first temperature sequence corresponding to the temperature collected by the target temperature sensing module within a preset time period, and the second temperature sequence corresponding to the temperature collected by the preset temperature sensing module deployed in the storage location within a preset time period. The control module is used to perform correlation fitting on the first temperature series and the second temperature series, and determine whether to use the initial position to be measured as the optimal placement position of the target temperature sensing module based on the correlation fitting result; and to use the real-time temperature collected by the target temperature sensing module at the optimal placement position as the real-time temperature of the chemical formation process workshop.

[0013] In conjunction with the second aspect, in one implementation, the control module is specifically used for: The correlation coefficient is obtained by performing correlation fitting on the first temperature series and the second temperature series; If the correlation coefficient is greater than the preset correlation coefficient threshold, then the initial position to be measured is taken as the optimal placement position of the target temperature sensing module. If the correlation coefficient is less than or equal to the correlation coefficient threshold, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and the initial position to be measured for placing the target temperature sensing module will be re-determined.

[0014] In conjunction with the second aspect, in one embodiment, the number of target temperature sensing modules is multiple, and the control module is specifically used for: For each target temperature sensing module, the first and second temperature series corresponding to it are fitted to obtain the correlation coefficient; Sort all correlation coefficients in descending order and determine whether the target correlation coefficient at the top is greater than the correlation coefficient threshold. If so, the initial location of the target temperature sensing module corresponding to the target correlation coefficient is taken as the optimal deployment location. If not, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and a new initial position to be measured for placing the target temperature sensing module will be determined.

[0015] In conjunction with the second aspect, in one implementation, the fitting method is linear fitting or nonlinear parametric regression.

[0016] In conjunction with the second aspect, in one embodiment, the deployment module is further configured to: The target deployment space is determined based on the airflow information of the formation cabinet's location in the formation process workshop and the height of the formation cabinet. The initial position of the target temperature sensing module within the target deployment space is determined based on the preset measurable spatial range of the target temperature sensing module and the target deployment space.

[0017] In conjunction with the second aspect, in one embodiment, if the optimal placement position of the target temperature sensing module is the initial position to be measured, the control module is further configured to: The target sequence is obtained by subtracting the first temperature sequence and the second temperature sequence; Construct a normal probability graph based on the target sequence; Target data is selected from the normal probability graph by pre-setting confidence intervals, and compensation values ​​are determined based on the target data; The compensation value is used to compensate for the real-time temperature collected by the target temperature sensing module at the optimal deployment location.

[0018] In conjunction with the second aspect, in one implementation, the control module is further configured to: Acquire real-time environmental parameters, including humidity and airflow velocity; A new compensation value is determined based on the real-time environmental parameters and the compensation value, and the step of compensating the real-time temperature collected by the target temperature sensing module at the optimal deployment position based on the compensation value is performed based on the new compensation value.

[0019] Thirdly, embodiments of this application provide a battery workshop temperature control device, which includes a processor, a memory, and a battery workshop temperature control program stored in the memory and executable by the processor. When the battery workshop temperature control program is executed by the processor, it implements the steps of the aforementioned battery workshop temperature control method.

[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing a battery shop temperature control program, wherein when the battery shop temperature control program is executed by a processor, it implements the steps of the aforementioned battery shop temperature control method.

[0021] The beneficial effects of the technical solutions provided in this application include: The target temperature sensing module is deployed at the initial test location within the formation process workshop. A first temperature sequence corresponding to the temperature collected by the target temperature sensing module within a preset time period is obtained, along with a second temperature sequence corresponding to the temperature collected by a preset temperature sensing module deployed within the storage location within a preset time period. Correlation fitting is then performed on the first and second temperature sequences. Based on the correlation fitting results, it is determined whether the initial test location has the best correlation and stability with the storage location. If so, this location is taken as the optimal deployment location for the target temperature sensing module, ensuring that the real-time temperature collected by the target temperature sensing module accurately represents the actual battery temperature within the storage location. Therefore, the real-time temperature collected by the target temperature sensing module at the optimal deployment location can be used as the real-time temperature of the formation process workshop. Thus, this application determines the optimal deployment location of the temperature sensing module that best reflects the overall temperature change of the storage location in real time by comparing the correlation between the temperature at the test location and the battery temperature in the storage location. This solves the technical problem in the prior art where the random deployment of temperature probes within the formation process workshop results in an inaccurate representation of the actual battery temperature within the storage location. Attached Figure Description

[0022] Figure 1This is a flowchart illustrating an embodiment of the battery workshop temperature control method of this application; Figure 2 This is a schematic diagram illustrating the temperature difference between inside and outside the warehouse in the embodiments of this application; Figure 3 This is a top view of the formation process workshop involved in the embodiments of this application; Figure 4 This is a schematic diagram of the average temperature series at different locations involved in the embodiments of this application; Figure 5 This is a schematic diagram of the correlation fitting result matrix involved in the embodiments of this application; Figure 6 This is a schematic diagram of the target number sequence involved in the embodiments of this application; Figure 7 This is a schematic diagram of the normal probability distribution involved in the embodiments of this application; Figure 8 This is a schematic diagram of the hardware structure of the battery workshop temperature control equipment involved in the embodiments of this application. Detailed Implementation

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

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0025] In a first aspect, embodiments of this application provide a method for temperature control in a battery workshop.

[0026] In one embodiment, reference is made to Figure 1 , Figure 1 This is a schematic flowchart illustrating an embodiment of the battery workshop temperature control method of this application. Figure 1 As shown, the temperature control methods in the battery workshop include: Step S10: Deploy the target temperature sensing module at the initial position to be measured in the formation process workshop.

[0027] As an example, it is understandable that the formation process workshop is a closed environment where temperature and humidity must be strictly controlled. See [reference needed]. Figure 2As shown, the storage locations are arranged in a cabinet-like layout, with rows and layers. Because the batteries generate heat spontaneously during the formation process within the storage locations, the battery temperature inside each location is higher than the ambient temperature outside. The temperature distribution is as follows: the batteries in the middle of the formation cabinet have the highest temperature, the surrounding areas have the lowest temperature, and the outside of the formation cabinet has the lowest temperature. Each storage location within the formation cabinet has its own temperature sensing module (i.e., a preset temperature sensing module, such as a temperature probe) that records the battery temperature in that location in real time; see [link to documentation]. Figure 2 As shown, taking a 5×10 formation cabinet as an example, there are 50 temperature probes in total. The average battery temperature in its storage location over a certain period of time can be calculated by collecting the temperature from each temperature probe.

[0028] However, the temperature control in the formation process workshop relies on the temperature displayed on a digital display panel installed outside the workshop (i.e., digital temperature). This digital temperature is collected by a single temperature probe randomly suspended somewhere inside the workshop. This probe records the ambient temperature and displays it on the digital display panel in real time, then uploads the temperature data to the MES (Manufacturing Execution System). The plant management adjusts the return air temperature of the formation process workshop in real time based on the digital temperature to achieve overall temperature balance. Therefore, the target temperature sensing module in this embodiment refers to the temperature sensing module that needs to be connected to the digital display panel. It can be a temperature probe or other devices capable of temperature acquisition; this is not limited here. However, its installation location is not randomly determined but needs to be determined through correlation calculations in this embodiment. It should be noted that the initial measurement location refers to the initially determined location in the formation process workshop for installing the target temperature sensing module. This location can be determined based on actual needs or experience, such as using the extension of the central axis of the formation cabinet at a height of 2 meters as the initial measurement location (e.g.,...). Figure 3 (The positions corresponding to points a to f in the diagram).

[0029] In this embodiment, the target temperature sensing module is initially installed at a specific initial measurement location so that it can collect temperature data within the chemical formation workshop at that location, providing a data foundation for subsequent correlation calculations. For example, the target temperature sensing module is suspended... Figure 3 At point a in the diagram, the target temperature sensing module will collect the temperature data of the chemical formation process workshop at point a.

[0030] Step S20: Obtain the first temperature sequence corresponding to the temperature collected by the target temperature sensing module within a preset time period, and the second temperature sequence corresponding to the temperature collected by the preset temperature sensing module deployed in the storage location within a preset time period.

[0031] As an example, it should be noted that the specific value of the preset duration can be determined according to actual needs and is not limited here. For example, if the preset duration is 2 weeks, then the target temperature sensing module and the preset temperature sensing modules located in each storage location will collect temperature data at a preset frequency during these 2 weeks, such as collecting temperature data 6 times per hour. Based on this, the six temperature data collected by the target temperature sensing module are averaged hourly to obtain the first average temperature value for each hour, thereby generating the first temperature series of the target temperature sensing module within the preset duration. At the same time, the six temperature data collected by the preset temperature sensing modules in all storage locations are obtained hourly. For example, if there are 50 preset temperature sensing modules, then there are 300 temperature data points for each hour. The 300 temperature data points are averaged to obtain the second average temperature value for each hour, thereby generating the second temperature series of the preset temperature sensing modules within the preset duration.

[0032] Step S30: Perform correlation fitting on the first temperature series and the second temperature series, and determine whether to use the initial position to be measured as the optimal placement position of the target temperature sensing module based on the correlation fitting result.

[0033] As an example, in this embodiment, correlation fitting is performed on the first and second temperature series to determine the correlation between the temperature corresponding to the initial position to be measured and the average battery temperature in the storage location. A higher correlation indicates a stronger and more stable correlation between the initial position to be measured and the storage location, meaning the temperature collected by the target temperature sensing module at the initial position to be measured is closer to the actual battery temperature in the storage location. Based on this, the optimal placement location for the target temperature sensing module can be determined according to the correlation level; a higher correlation suggests that the initial position to be measured is more likely to be the optimal placement location for the target temperature sensing module. It should be noted that before performing correlation fitting, a small number of prominent outliers in the first and second temperature series can be uniformly removed before fitting to improve the accuracy of the fitting results.

[0034] Step S40: The real-time temperature collected by the target temperature sensing module at the optimal deployment location is used as the real-time temperature of the chemical formation process workshop.

[0035] As an example, in this embodiment, since the optimal placement location has the best correlation and stability with the storage location, it can most accurately reflect the overall temperature change of the storage location in real time. Therefore, the real-time temperature collected by the target temperature sensing module at the optimal placement location is closest to the actual battery temperature in the actual storage location. Based on this, the real-time temperature collected by the target temperature sensing module at the optimal placement location is used as the real-time temperature of the formation process workshop to accurately characterize the actual battery temperature in the actual storage location. This solves the technical problem in the prior art where the use of random placement of temperature probes in the formation process workshop results in an inaccurate characterization of the actual battery temperature in the actual storage location.

[0036] Furthermore, in one embodiment, before the step of deploying the target temperature sensing module at the initial position to be measured within the formation process workshop, the method further includes: The target deployment space is determined based on the airflow information of the formation cabinet's location in the formation process workshop and the height of the formation cabinet. The initial position of the target temperature sensing module within the target deployment space is determined based on the preset measurable spatial range of the target temperature sensing module and the target deployment space.

[0037] As an example, it is understandable that the stability of the temperature sensing module and the average temperature it collects are easily affected by its placement location, height, and airflow. It should be placed at a moderate height in a location without significant airflow. In addition, since the formation cabinet has a fan for heat dissipation, areas too close to the formation cabinet often cannot maintain stability. Based on this, the target placement space can be determined according to the airflow information of the formation cabinet's location in the formation process workshop and the height of the formation cabinet. For example, the target placement space can be the range of 2 to 10 meters away from the formation cabinet and 2 to 3 meters above the ground, with the central axis of the formation cabinet as the center line. That is, the initial position to be measured can only be selected within the target placement space, effectively eliminating placement positions with poor accuracy, reducing the number of correlation comparisons required, and improving the efficiency of selecting the best placement position. Then, based on the preset measurable space range of the target temperature sensing module, a suitable position is initially selected from the target placement space as the initial position to be measured.

[0038] It should be noted that the preset measurable space range refers to the maximum space in which the temperature sensing module can accurately collect temperature data. Its specific value can be determined based on the physical properties of the temperature sensing module, environmental factors, and installation method, or it can be given directly based on experience. For example, if the preset measurable space range of a certain temperature sensing module is a three-dimensional space with a length, width, and height of 3 meters centered on the temperature sensing module, then the temperature sensing module can accurately collect the temperature at any location within that three-dimensional space.

[0039] It is worth noting that if correlation calculations need to be performed on multiple locations simultaneously, the number of target temperature sensing modules and the initial measurement location corresponding to each target temperature sensing module can be determined based on the preset measurable spatial range of the target temperature sensing module and the size of the target deployment space; for example, see Figure 3 As shown, there are six target temperature sensing modules, and the initial positions to be measured for each target temperature sensing module are a, b, c, d, e, and f, respectively.

[0040] Furthermore, in one embodiment, the fitting method is linear fitting or nonlinear parametric regression.

[0041] In this exemplary embodiment, the fitting method is preferably linear fitting or nonlinear parametric regression, but other methods capable of achieving correlation fitting can also be used, and are not limited here. It should be noted that the calculation methods and principles of linear fitting and nonlinear parametric regression are common knowledge in the art, and will not be elaborated upon here for the sake of brevity. In this embodiment, linear fitting will be used as an example to briefly describe the process of correlation fitting between the first and second temperature series: Assuming the first temperature series t = [t1, t2, t3, t4, t5] and the second temperature series T = [T1, T2, T3, T4, T5], the mean of series t is first calculated as... And the mean of the sequence T is Using the least squares method and based on the sequence t and its mean Sequence T and its mean The slope A and intercept B are calculated to generate the regression equation Y = AX + B; based on this regression equation, the predicted temperature values ​​corresponding to each term in the sequence t are then calculated. This yields a series of predicted temperature values. Then, using this series of predicted temperature values, the series T, and the mean... Calculate the total sum of squares (TSS) and the regression sum of squares (ESS); finally, calculate the correlation coefficient R based on the total sum of squares (TSS) and the regression sum of squares (ESS). 2 And the correlation coefficient R 2 The magnitude of the value indicates the degree of correlation between the temperature at the initial location to be measured and the average battery temperature in the storage location.

[0042] Further, in one embodiment, the step of performing correlation fitting on the first temperature series and the second temperature series, and determining whether to use the initial position to be measured as the optimal placement position of the target temperature sensing module based on the fitting result, includes: The correlation coefficient is obtained by performing correlation fitting on the first temperature series and the second temperature series; If the correlation coefficient is greater than the preset correlation coefficient threshold, then the initial position to be measured is taken as the optimal placement position of the target temperature sensing module. If the correlation coefficient is less than or equal to the correlation coefficient threshold, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and the initial position to be measured for placing the target temperature sensing module will be re-determined.

[0043] As an example, it should be noted that the specific value of the correlation coefficient threshold can be determined according to actual needs and is not limited here. For example, the correlation coefficient threshold can be set to 0.3. In this embodiment, the correlation coefficient R can be obtained by performing correlation fitting on the first temperature series and the second temperature series using linear fitting or nonlinear parametric regression methods. 2 Then determine the correlation coefficient R. 2 If the correlation coefficient R is greater than 0.3, it indicates a high correlation and good stability between the initial location to be measured and the storage location, in which case the initial location to be measured can be used as the optimal placement location for the target temperature sensing module; however, if the correlation coefficient R is less than 0.3, it indicates a high correlation between the initial location to be measured and the storage location, in which case the initial location to be measured can be used as the optimal placement location for the target temperature sensing module. 2 If the correlation coefficient R is less than or equal to 0.3, it indicates that the correlation between the initial location to be measured and the storage location is not high and the stability is poor. Therefore, the initial location to be measured is not considered the optimal placement location for the target temperature sensing module. A new initial location to be measured needs to be determined to place the target temperature sensing module, and the aforementioned steps need to be repeated until the correlation coefficient R corresponding to the temperature data at a certain location is found. 2 Until it is greater than 0.3.

[0044] Further, in one embodiment, the number of target temperature sensing modules is multiple, and the step of performing correlation fitting on the first temperature series and the second temperature series, and determining whether to use the initial position to be measured as the optimal placement position of the target temperature sensing module based on the fitting result, includes: For each target temperature sensing module, the first and second temperature series corresponding to it are fitted to obtain the correlation coefficient; Sort all correlation coefficients in descending order and determine whether the target correlation coefficient at the top is greater than the correlation coefficient threshold. If so, the initial location of the target temperature sensing module corresponding to the target correlation coefficient is taken as the optimal deployment location. If not, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and a new initial position to be measured for placing the target temperature sensing module will be determined.

[0045] As an example, in this embodiment, if there are multiple target temperature sensing modules, then each target temperature sensing module is suspended at its corresponding initial measurement position; for example, if there are six target temperature sensing modules, then each of the six target temperature sensing modules is suspended at... Figure 3 Points a, b, c, d, e, and f are located at the extension of the central axis of the formation cabinet, with points a and d 2 meters away from the formation cabinet, points b and e 4 meters away, and points c and f 6 meters away. The suspension height is uniformly 2.5 meters from the ground. Temperature data measured by each target temperature sensing module and the preset temperature sensing module are collected within a preset duration of continuous production. Based on the actual sampling frequency, the average temperature of each target temperature sensing module is calculated hourly to obtain the first temperature sequence corresponding to each location, denoted as {ta}, {tb}, {tc}, ..., {tf}. That is, {tah}, {tbh}, {tch}...{tfh}, where a, b, c, d, e, and f represent the corresponding initial positions to be measured, and h represents the average temperature data collected in the h-th hour; for example, in the 10th hour after the start of data collection, the average temperature of the target temperature sensing module at point a in the 10th hour is ta10. Assuming that in the 10th hour, the target temperature sensing module at point a collected six temperature data points of 40.0, 40.1, 40.1, 40.3, 40.3, and 40.4, then ta10 = 40.2.

[0046] Similarly, based on the actual sampling frequency, the average temperature of all storage locations within each hour is calculated to obtain the second temperature series, denoted as {T}, or {Th}, where h represents the average temperature data collected in the h-th hour. For example, in the 10th hour after the start of data collection, the average temperature corresponding to all temperature data collected by the 50 preset temperature sensing modules within the 10th hour is T10. Based on this, the following can be obtained: Figure 4 The table shown provides a feature value table where, for each h, a unique feature value represents the temperature at each initial measurement location and the actual temperature of the battery within the storage location, facilitating subsequent fitting. It should be noted that... Figure 4 The simplified display shows the average temperature at three locations (a, b, and c) and the average temperature of the batteries within the storage location.

[0047] Based on this, the first temperature series corresponding to each target temperature sensing module is linearly fitted to the second temperature series or fitted using nonlinear parameter regression to obtain multiple correlation coefficients. For example, {tah}, {tbh}, {tch}...{tfh} are fitted to {Th} respectively to obtain the correlation coefficient R. a 2 R b 2 Rc 2 ...R f 2 Then sort R in descending order. a 2 R b 2 R c 2 ...R f 2 Sort the data and determine if the correlation coefficient of the top-ranked target (i.e., the maximum correlation coefficient) is greater than 0.3. If so, it indicates that the initial location of the target temperature sensing module corresponding to this correlation coefficient has the best correlation and stability with the storage location. Therefore, this initial location is taken as the optimal deployment location. For example, see [link to relevant documentation]. Figure 5 As shown, R b 2 If the correlation coefficient is 33.9% > 0.3, then the initial location b is selected as the optimal placement location, and the other locations are discarded. However, if the correlation coefficient of the top-ranked target is ≤ 0.3, it indicates that none of the current initial locations have a high correlation with the storage location and are not very stable. In other words, no initial location with the best correlation and stability has been found among the six initial locations. Therefore, it is necessary to re-confirm the initial locations to be verified based on the actual situation on site and retest until a correlation coefficient corresponding to the temperature data at a certain location satisfies R. 2 Until >0.3.

[0048] Furthermore, in one embodiment, if the optimal placement position of the target temperature sensing module is the initial position to be measured, the method further includes: The target sequence is obtained by subtracting the first temperature sequence and the second temperature sequence; Construct a normal probability graph based on the target sequence; Target data is selected from the normal probability graph by pre-setting confidence intervals, and compensation values ​​are determined based on the target data; The compensation value is used to compensate for the real-time temperature collected by the target temperature sensing module at the optimal deployment location.

[0049] As an example, it is understandable that the temperature distribution within the formation process workshop is not uniform, leading to a quantitative deviation between the collected temperature data and the actual battery temperature. Therefore, to further improve the accuracy of temperature characterization, this embodiment will calculate and analyze the difference between the temperature at the optimal placement location and the average battery temperature in the storage area. The average difference will be used as a compensation value to establish a connection between the temperature collected by the target temperature sensing module and the average battery temperature in the storage area, facilitating real-time temperature control by plant management based on this data. It should be noted that the specific value of the preset confidence interval can be determined according to actual needs and is not limited here; for example, the preset confidence interval can be set to a 95% confidence interval.

[0050] Specifically, the difference between the sequence Th and the sequence tbh under the same h is taken to obtain the following: Figure 6 The target sequence {Th-tbh} is shown; then the target sequence {Th-tbh} is processed as follows: Figure 7 The normal probability plot is shown, and a target confidence interval is extracted from the normal probability plot based on the 95% confidence interval. The data in the target confidence interval is used as the target data. Then, the target data is averaged to obtain a compensation value. For example, if the mean of the target data is 2.8, then 2.8 is used as the final compensation value. Finally, the compensation value is used to compensate for the real-time temperature collected by the target temperature sensing module at the optimal deployment location to obtain the target real-time temperature, which is then used as the real-time temperature of the chemical formation process workshop. It is worth noting that the temperature series used to calculate the compensation value can be historical data or real-time data acquired within a certain period of time; there is no limitation here.

[0051] Furthermore, in one embodiment, after the step of determining the compensation value based on the target data, the method further includes: Acquire real-time environmental parameters, including humidity and airflow velocity; A new compensation value is determined based on the real-time environmental parameters and the compensation value, and the step of compensating the real-time temperature collected by the target temperature sensing module at the optimal deployment position based on the compensation value is performed based on the new compensation value.

[0052] As an example, it is understood that changes in humidity, airflow velocity, etc., also have a certain impact on temperature. Therefore, this embodiment incorporates real-time environmental parameters into the calculation of the compensation value to further improve accuracy. It should be noted that real-time environmental parameters include, but are not limited to, humidity and airflow velocity.

[0053] Specifically, this embodiment will introduce an adaptive weighting function to determine the new compensation value. This adaptive weighting function can be:

[0054] In the formula, This represents the new compensation value. H represents the compensation value; H represents the real-time humidity, H0 represents the humidity baseline value (such as the historical average humidity), and H... max This represents the maximum humidity threshold, which can be determined based on actual needs. V represents the humidity weighting coefficient; V represents the real-time airflow velocity, and V0 represents the baseline airflow velocity (e.g., the historical average airflow velocity). max This represents the maximum airflow velocity threshold, which can be determined according to actual needs. This represents the airflow velocity weighting coefficient; it should be noted that... and It can be calculated from historical data.

[0055] Based on this, real-time humidity and real-time airflow velocity are monitored, and the acquired real-time humidity, real-time airflow velocity and the compensation value calculated above are substituted into the above adaptive weighting function to calculate a new compensation value. The new compensation value is then used to compensate the real-time temperature collected by the target temperature sensing module at the optimal deployment position to obtain a new target real-time temperature. The new target real-time temperature is then used as the real-time temperature of the formation process workshop to accurately represent the actual temperature of the batteries in the actual storage location.

[0056] Secondly, embodiments of this application also provide a battery workshop temperature control device.

[0057] In one embodiment, the battery workshop temperature control device includes: The deployment module is used to deploy the target temperature sensing module at the initial position to be measured in the chemical formation process workshop. The acquisition module is used to acquire the first temperature sequence corresponding to the temperature collected by the target temperature sensing module within a preset time period, and the second temperature sequence corresponding to the temperature collected by the preset temperature sensing module deployed in the storage location within a preset time period. The control module is used to perform correlation fitting on the first temperature series and the second temperature series, and determine whether to use the initial position to be measured as the optimal placement position of the target temperature sensing module based on the correlation fitting result; and to use the real-time temperature collected by the target temperature sensing module at the optimal placement position as the real-time temperature of the chemical formation process workshop.

[0058] Furthermore, in one embodiment, the control module is specifically used for: The correlation coefficient is obtained by performing correlation fitting on the first temperature series and the second temperature series; If the correlation coefficient is greater than the preset correlation coefficient threshold, then the initial position to be measured is taken as the optimal placement position of the target temperature sensing module. If the correlation coefficient is less than or equal to the correlation coefficient threshold, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and the initial position to be measured for placing the target temperature sensing module will be re-determined.

[0059] Furthermore, in one embodiment, the number of target temperature sensing modules is multiple, and the control module is specifically used for: For each target temperature sensing module, the first and second temperature series corresponding to it are fitted to obtain the correlation coefficient; Sort all correlation coefficients in descending order and determine whether the target correlation coefficient at the top is greater than the correlation coefficient threshold. If so, the initial location of the target temperature sensing module corresponding to the target correlation coefficient is taken as the optimal deployment location. If not, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and a new initial position to be measured for placing the target temperature sensing module will be determined.

[0060] Furthermore, in one embodiment, the fitting method is linear fitting or nonlinear parametric regression.

[0061] Furthermore, in one embodiment, the deployment module is also used for: The target deployment space is determined based on the airflow information of the formation cabinet's location in the formation process workshop and the height of the formation cabinet. The initial position of the target temperature sensing module within the target deployment space is determined based on the preset measurable spatial range of the target temperature sensing module and the target deployment space.

[0062] Furthermore, in one embodiment, if the optimal placement position of the target temperature sensing module is the initial position to be measured, the control module is further configured to: The target sequence is obtained by subtracting the first temperature sequence and the second temperature sequence; Construct a normal probability graph based on the target sequence; Target data is selected from the normal probability graph by pre-setting confidence intervals, and compensation values ​​are determined based on the target data; The compensation value is used to compensate for the real-time temperature collected by the target temperature sensing module at the optimal deployment location.

[0063] Furthermore, in one embodiment, the control module is also used for: Acquire real-time environmental parameters, including humidity and airflow velocity; A new compensation value is determined based on the real-time environmental parameters and the compensation value, and the step of compensating the real-time temperature collected by the target temperature sensing module at the optimal deployment position based on the compensation value is performed based on the new compensation value.

[0064] The functions of each module in the above-mentioned battery workshop temperature control device correspond to the steps in the above-mentioned battery workshop temperature control method embodiment, and their functions and implementation processes will not be described in detail here.

[0065] Thirdly, this application provides a battery workshop temperature control device, which can be a personal computer (PC), laptop computer, server or other device with data processing capabilities.

[0066] Reference Figure 8 , Figure 8 This is a schematic diagram of the hardware structure of the battery workshop temperature control equipment involved in the embodiments of this application. In the embodiments of this application, the battery workshop temperature control equipment may include a processor, a memory, a communication interface, and a communication bus.

[0067] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0068] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the battery shop temperature control equipment, as well as interfaces used for interconnecting the battery shop temperature control equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0069] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0070] The processor can be a general-purpose processor, which can call the battery room temperature control program stored in the memory and execute the battery room temperature control method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the battery room temperature control program is called can be referred to in the various embodiments of the battery room temperature control method of this application, and will not be repeated here.

[0071] Those skilled in the art will understand that Figure 8 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0072] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0073] The present application has a readable storage medium storing a battery shop temperature control program, wherein when the battery shop temperature control program is executed by a processor, it implements the steps of the battery shop temperature control method described above.

[0074] The method implemented when the battery workshop temperature control procedure is executed can be referred to in various embodiments of the battery workshop temperature control method of this application, and will not be repeated here.

[0075] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0076] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0077] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0078] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0079] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0080] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0081] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for temperature control in a battery workshop, characterized in that, Includes the following steps: The target temperature sensing module is deployed at the initial position to be measured within the chemical formation process workshop; Acquire the first temperature sequence corresponding to the temperature collected by the target temperature sensing module within a preset time period, and the second temperature sequence corresponding to the temperature collected by the preset temperature sensing module deployed in the storage location within a preset time period. The first temperature series and the second temperature series are subjected to correlation fitting, and the correlation fitting results are used to determine whether the initial position to be measured is the optimal placement position of the target temperature sensing module. The real-time temperature collected by the target temperature sensing module at the optimal deployment location is used as the real-time temperature of the chemical formation process workshop.

2. The battery workshop temperature control method as described in claim 1, characterized in that, The step of performing correlation fitting on the first temperature series and the second temperature series, and determining whether to use the initial position to be measured as the optimal placement position of the target temperature sensing module based on the fitting result, includes: The correlation coefficient is obtained by performing correlation fitting on the first temperature series and the second temperature series; If the correlation coefficient is greater than the preset correlation coefficient threshold, then the initial position to be measured is taken as the optimal placement position of the target temperature sensing module. If the correlation coefficient is less than or equal to the correlation coefficient threshold, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and a new initial position to be measured for placing the target temperature sensing module will be determined.

3. The battery workshop temperature control method as described in claim 1, characterized in that, The number of the target temperature sensing modules is multiple; The step of performing correlation fitting on the first temperature series and the second temperature series, and determining whether to use the initial position to be measured as the optimal placement position of the target temperature sensing module based on the fitting result, includes: For each target temperature sensing module, the first and second temperature series corresponding to it are fitted to obtain the correlation coefficient; Sort all correlation coefficients in descending order and determine whether the target correlation coefficient at the top is greater than the correlation coefficient threshold. If so, the initial location of the target temperature sensing module corresponding to the target correlation coefficient is taken as the optimal deployment location. If not, the initial position to be measured will not be used as the optimal placement position for the target temperature sensing module, and a new initial position to be measured for placing the target temperature sensing module will be determined.

4. The battery workshop temperature control method according to any one of claims 1 to 3, characterized in that: The fitting method is either linear fitting or nonlinear parametric regression.

5. The battery workshop temperature control method as described in claim 1, characterized in that, Before the step of deploying the target temperature sensing module at the initial location to be measured within the chemical formation process workshop, the method further includes: The target deployment space is determined based on the airflow information of the formation cabinet's location in the formation process workshop and the height of the formation cabinet. The initial position of the target temperature sensing module within the target deployment space is determined based on the preset measurable spatial range of the target temperature sensing module and the target deployment space.

6. The battery workshop temperature control method as described in claim 1, characterized in that, If the optimal placement position of the target temperature sensing module is the initial position to be measured, the method further includes: The target sequence is obtained by subtracting the first temperature sequence and the second temperature sequence; Construct a normal probability graph based on the target sequence; Target data is selected from the normal probability graph by pre-setting confidence intervals, and compensation values ​​are determined based on the target data; The compensation value is used to compensate for the real-time temperature collected by the target temperature sensing module at the optimal deployment location.

7. The battery workshop temperature control method as described in claim 6, characterized in that, After the step of determining the compensation value based on the target data, the method further includes: Acquire real-time environmental parameters, including humidity and airflow velocity; A new compensation value is determined based on the real-time environmental parameters and the compensation value, and the step of compensating the real-time temperature collected by the target temperature sensing module at the optimal deployment position based on the compensation value is performed based on the new compensation value.

8. A temperature control device for a battery workshop, characterized in that, include: The deployment module is used to deploy the target temperature sensing module at the initial position to be measured in the chemical formation process workshop. The acquisition module is used to acquire the first temperature sequence corresponding to the temperature collected by the target temperature sensing module within a preset time period, and the second temperature sequence corresponding to the temperature collected by the preset temperature sensing module deployed in the storage location within a preset time period. The control module is used to perform correlation fitting on the first temperature series and the second temperature series, and determine whether to use the initial position to be measured as the optimal placement position of the target temperature sensing module based on the correlation fitting result. The real-time temperature collected by the target temperature sensing module at the optimal deployment location is used as the real-time temperature of the chemical formation process workshop.

9. A temperature control device for a battery workshop, characterized in that, The battery shop temperature control device includes a processor, a memory, and a battery shop temperature control program stored in the memory and executable by the processor, wherein when the battery shop temperature control program is executed by the processor, it implements the steps of the battery shop temperature control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a battery shop temperature control program, wherein when the battery shop temperature control program is executed by a processor, it implements the steps of the battery shop temperature control method as described in any one of claims 1 to 7.

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