A method, device and medium for preparing sodium ion battery with integrated temperature monitoring

Abstract

The invention discloses a method, device and medium for preparing a sodium ion battery with integrated temperature monitoring, and relates to the technical field of energy storage equipment. The method collects the importance coefficient of a thermosensitive and high temperature resistant sensor, a temperature difference value monitored in a region, a regional thermal imbalance index and a regional temperature mean, establishes a data analysis model, generates a regional evaluation coefficient, and compares it with a preset regional threshold value to obtain a comparison result. The preset region that needs to be adjusted is determined according to the comparison result, and then the number of preset regions that need to be adjusted and the corresponding regional evaluation coefficients and the number of preset regions that do not need to be adjusted and the corresponding regional evaluation coefficients are counted to obtain a regional adjustment result. The adjusted preset regions are counted and compared with the adjustment threshold value. According to the comparison result, a time series model is used to perform power distribution prediction and analysis, and the regional temperature control state and monitoring accuracy are accurately evaluated to avoid excessive thermal energy supplementation and save resources.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage devices, and more specifically to a method, device and medium for preparing a sodium ion battery with integrated temperature monitoring. Background Art

[0002] As a new energy storage technology, sodium-ion batteries have attracted widespread attention in recent years due to their abundant resources, low cost and environmental friendliness. Compared with traditional lithium-ion batteries, the main materials of sodium-ion batteries are widely available and are particularly suitable for large-scale energy storage applications. However, during the preparation process of sodium-ion batteries, there are problems such as performance degradation and safety hazards caused by temperature changes, which has become one of the key technical bottlenecks restricting its further development.

[0003] The prior art has the following deficiencies:

[0004] At present, regarding a preparation method of a sodium ion battery, especially in the preparation process of the positive electrode material, it is necessary to dry the precursor and then calcine it at 800-900℃ in an air atmosphere for 10 to 12 hours. Due to the long time and high temperature, the accuracy of temperature monitoring in the preparation process is often missing, resulting in low crystallinity and low crystal purity of the material. At the same time, there is an insufficient problem of stress release during the cooling period, which reduces the material performance and thus affects the battery life and battery performance. Therefore, a preparation method of a sodium ion battery with integrated temperature monitoring is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for preparing a sodium ion battery with integrated temperature monitoring, which solves the problems raised in the above-mentioned background technology by using different product inspection methods.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for preparing a sodium ion battery with integrated temperature monitoring, comprising S1: collecting regional monitoring feature information and regional temperature change characteristics, and obtaining the importance coefficient of the thermosensitive high temperature sensor, the monitored temperature difference value in the region, the regional thermal imbalance index and the regional temperature mean through normalization processing;

[0008] S2: According to the importance coefficient of thermistor and high temperature resistant sensor, the difference of monitored temperature difference in the region, the regional thermal imbalance index and the regional temperature mean, a data analysis model is established to generate the regional evaluation coefficient;

[0009] S3: Obtaining a regional evaluation coefficient and comparing it with a preset regional threshold to obtain a comparison result, determining the preset region that needs to be adjusted according to the comparison result, and then counting the number of preset regions that need to be adjusted and the corresponding regional evaluation coefficients and the number of preset regions that do not need to be adjusted and the corresponding regional evaluation coefficients to obtain a regional adjustment result;

[0010] S4: Obtain the regional adjustment results, count the adjusted preset areas, and compare them with the adjustment thresholds. Based on the comparison results, determine to conduct power distribution forecast analysis through the time series model to further refine and optimize the thermal energy supplement distribution plan.

[0011] In a preferred embodiment, the regional monitoring characteristic information includes the importance coefficient of the thermosensitive high temperature resistant sensor and the monitored temperature difference value within the region; the regional temperature change characteristics include the regional thermal imbalance index and the regional temperature mean;

[0012] The importance coefficient of the thermistor is obtained by weighted calculation based on the temperature standard deviation of the timestamp in the area corresponding to the sensor, the response time of the sensor, and the difference between the temperature value monitored by the sensor and the temperature control reaction temperature of the furnace. ; Where i is the i-th timestamp and g is the g-th preset area;

[0013] The temperature data of the area is collected by each sensor in the area in real time, and the maximum temperature collected by the sensor in the area is subtracted from the minimum temperature to obtain the monitored temperature difference value in the area. ;

[0014] The temperature data of multiple monitoring points in the preset area are collected in real time by sensors, and then the maximum and minimum temperature difference in the area are calculated. The standard deviation reflects the degree of temperature difference in the area, and the regional thermal imbalance index is obtained by substituting the weighted average. ;

[0015] Through temperature data collection, the temperature data is accumulated and the ratio is calculated by the number of temperature data to obtain the regional temperature average. .

[0016] In a preferred embodiment, the importance coefficient of the thermosensitive high temperature sensor, the difference in monitored temperature difference within the region, the regional thermal imbalance index and the regional temperature mean are obtained to establish a data analysis model to generate a regional evaluation coefficient. , based on the formula:

[0017] ;

[0018] In the formula, is the regional assessment coefficient, , , as well as They are the importance coefficients of thermistor and high temperature resistant sensor , monitor the temperature difference within the area , Regional Thermal Imbalance Index And the regional temperature mean The preset scaling factor of , , as well as Both are greater than 0.

[0019] In a preferred embodiment, after obtaining the regional evaluation coefficient, the regional evaluation coefficient is compared and analyzed with the continuously iterated regional threshold value;

[0020] If the regional evaluation coefficient is greater than or equal to the regional threshold, the current preset region is marked as a preset region that needs to be adjusted, and an adjustment signal is generated;

[0021] If the regional evaluation coefficient is less than the regional threshold, the current preset region is marked as a preset region that does not need to be adjusted, and a stable signal is generated.

[0022] In a preferred embodiment, the number of preset areas marked as needing adjustment and the corresponding regional evaluation coefficients are counted, and the preset areas marked as not needing adjustment and the corresponding regional evaluation coefficients are obtained and accumulated and calculated, which is specifically expressed as follows: , where n is the number of preset areas that need to be adjusted, , where m is the number of preset areas that do not need to be adjusted; the accumulated calculation results are weighted ratio calculated to obtain the preset area adjustment range. The specific formula is as follows:

[0023] ;

[0024] In the formula, Adjust the amplitude for the preset area. and The weights of the calculated results are respectively added for the preset areas marked as needing adjustment and the preset areas marked as not needing adjustment;

[0025] According to the preset area adjustment range, the reduced range of the preset area to be adjusted is determined to obtain the area adjustment result.

[0026] In a preferred embodiment, the time series model adopts an ARIMAX model. The specific steps of performing heat energy distribution prediction analysis using the time series model are as follows:

[0027] Step A1, obtaining prediction data;

[0028] Step A2, establishing an ARIMAX model;

[0029] Step A3, using the maximum likelihood estimation (MLE) method to estimate the ARIMAX model parameters;

[0030] Step A4, verify the effect of the fitting model and check the goodness of fit of the model by residual analysis;

[0031] Step A5, using the fitted model to predict the future heat energy supplement allocation plan, and taking the maximum value of the predicted result as the highest point of heat energy supplement in the future time period.

[0032] In a preferred embodiment, the exogenous variables in the ARIMAX model include the importance coefficient of the thermosensitive high temperature resistant sensor, the monitored temperature difference value in the region, the regional thermal imbalance index, the regional temperature mean, the heat conduction rate in the heat energy supplement range, and the heat convection rate in the heat energy supplement range. The ARIMAX model formula is:

[0033] ;

[0034] In the formula, is the heat energy supplement value at the current timestamp, is the value of the heat energy replenishment value at the current timestamp after i time units, is a constant term, is the i-th order autoregressive parameter, is the order of the autoregressive term, is the j-th order moving average parameter, is the order of the moving average term, is the white noise term, is the value of the white noise term after the lag of j time units, representing the random error, is an exogenous variable The coefficient of is the lag order of the exogenous variable, is an exogenous variable lagged by k periods.

[0035] In a preferred embodiment, in step A3, , , , , It is calculated by the maximum likelihood estimation method.

[0036] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above methods when executing the computer program.

[0037] On the other hand, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0038] Technical effects and advantages of the present invention:

[0039] 1. The present invention establishes a data analysis model by collecting the importance coefficient of thermistor and high-temperature resistant sensors, the monitored temperature difference value within the region, the regional thermal imbalance index and the regional temperature mean, generates a regional evaluation coefficient, and compares it with the preset regional threshold to obtain a comparison result, determines the preset area that needs to be adjusted according to the comparison result, and then counts the number of preset areas that need to be adjusted and the corresponding regional evaluation coefficients and the number of preset areas that do not need to be adjusted and the corresponding regional evaluation coefficients to obtain the regional adjustment result, accurately evaluates the regional temperature control state and monitoring accuracy, dynamically adjusts the regional range, and improves temperature uniformity.

[0040] 2. The present invention obtains the regional adjustment results, counts the adjusted preset areas, and compares them with the adjustment thresholds. Based on the comparison results, it determines to perform power distribution forecast analysis through a time series model, further refines and optimizes the heat energy supplement distribution plan, avoids excessive heat energy supplementation, saves resources, ensures normal grain growth, and improves crystallinity. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A method flow chart of a method for preparing a sodium ion battery with integrated temperature monitoring according to the present invention;

[0042] Figure 2 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] A method for preparing a sodium ion battery using sodium layered oxide (M is a transition metal), using solution co-precipitation method; specifically, the solution co-precipitation method is to select soluble compounds containing the components of the target material, dissolve these raw materials in deionized water, prepare a precursor solution of the required concentration, add a precipitant (such as ammonia solution, etc.) to the mixed precursor solution, control the reaction conditions (such as temperature, pH value and stirring speed) to promote the formation of precipitation, and after the precipitation is completed, place it at a certain temperature for aging, and finally separate the precipitate from the liquid phase by centrifugation or filtration. Subsequently, the precipitate is washed with deionized water or an appropriate solvent for multiple times to remove impurities and unreacted ions;

[0045] According to the chemical reaction equation: After the precursor is dried, it is calcined in an air atmosphere (temperature 800-900°C, time 10-12 hours) to prepare a positive electrode material with high cycle stability; then a hard carbon material is prepared by pyrolyzing biomass (such as cellulose, peel, etc.), wherein the pyrolysis reaction conditions are 600-1000°C, and it is carried out under the protection of an air atmosphere to obtain a microporous hard carbon, i.e., a negative electrode material, using sodium hexafluorophosphate. Prepare the electrolyte, and select ethylene carbonate (EC) and dimethyl carbonate (DMC) as solvents. Usually, the typical ratio is EC:DMC = 1:1 (volume ratio); select polypropylene (PP) or polyethylene (PE) as the separator material to enhance the thermal stability of the battery, and improve the electrolyte wettability through plasma surface modification;

[0046] The present invention provides a sodium ion battery preparation method with integrated temperature monitoring. A tube furnace, a box furnace or an atmosphere furnace is selected according to the material properties and the preparation scale to ensure that the heat source is effectively controlled, which is also the pre-condition for the software monitoring of the present invention. At the same time, the inner wall of the furnace should be made of high-temperature corrosion-resistant materials (such as alumina, quartz or stainless steel coating, etc.). The dried precursor material is evenly spread in a high-temperature resistant ceramic crucible or tray, and the temperature is raised to the above-mentioned calcination temperature in an air atmosphere at a rate of 2-5°C per minute;

[0047] The present disclosure is based on the preparation environment, that is, the surface of the prepared furnace, pre-installing multiple thermal-sensitive high-temperature sensors, analyzing the regional monitoring characteristics and regional temperature change characteristics corresponding to each thermal-sensitive high-temperature sensor, obtaining the regional evaluation coefficient, analyzing the regional adjustment results, and predicting and analyzing the supplementary temperature results of the thermal imbalance area according to the regional adjustment results and the time series model;

[0048] Example 1

[0049] See also Figure 1 , a method for preparing a sodium ion battery with integrated temperature monitoring, the specific operation process is as follows:

[0050] S1: Collect regional monitoring feature information and regional temperature change characteristics, and obtain the importance coefficient of thermistor and high temperature sensor, the monitored temperature difference value in the region, the regional thermal imbalance index and the regional temperature mean through normalization processing;

[0051] Among them, normalization is a common method of data preprocessing, which is used to convert data of different scales or ranges to a unified scale, so that the impact of each feature on subsequent analysis or model training is more balanced. In temperature monitoring data analysis, normalization helps to eliminate the dimensional differences between different features and ensure that each feature contributes to the model in a balanced manner;

[0052] Specifically, the normalization method is through Z-score standardization, and the specific formula is expressed as:

[0053] ;

[0054] In the formula, is the normalized original data, is the original data, is the mean of the original data, is the standard deviation of the original data;

[0055] The raw data include the importance coefficient of thermistor and high temperature sensor, the monitored temperature difference in the region, the regional thermal imbalance index and the regional temperature mean, which will not be elaborated here.

[0056] Specifically, the regional monitoring characteristic information includes the importance coefficient of the thermal-sensitive high-temperature resistant sensor and the monitored temperature difference value in the region; the regional temperature change characteristics include the regional thermal imbalance index and the regional temperature mean;

[0057] The importance coefficient of thermistor and high temperature sensor reflects the relative importance of the corresponding area of ​​each sensor in the entire temperature control system. It is usually based on the temperature stability, measurement accuracy and influence of the area where the sensor is located on the overall heating process. The acquisition logic is to obtain the importance coefficient of thermistor and high temperature sensor by weighted calculation through the temperature standard deviation of the timestamp in the area corresponding to the sensor, the response time of the sensor and the difference between the temperature value monitored by the sensor and the reaction temperature of the furnace temperature control. ; Where i is the i-th timestamp and g is the g-th preset area;

[0058] It should be noted that the timestamp was set by the experimenter based on the historical furnace temperature difference and historical material crystallinity, and will not be elaborated here;

[0059] The setting of the preset areas is set by the experimenter according to the physical structure, temperature distribution and temperature uniformity requirements of the furnace. There is no limit on the number of preset areas and the area of ​​each preset area.

[0060] Specifically, the temperature standard deviation of the timestamp in the area corresponding to the sensor is obtained by statistically calculating the temperature data of the sensor in the timestamp. The specific standard deviation formula is a prior art and will not be described in detail here.

[0061] The response time of the sensor refers to the time required for the temperature signal output by the sensor to reach the final steady-state ratio when the temperature changes. By inputting temperature changes, recording sensor data, and then fitting the data to the response curve, the fitting response curve is exponential, which conforms to the following formula:

[0062] ;

[0063] In the formula, is the temperature value of the sensor at time t, is the final steady-state temperature, For time, The time constant preset for the system;

[0064] The difference between the sensor monitoring temperature value and the furnace temperature control reaction temperature is obtained by subtracting the target temperature set by the temperature control system from the sensor temperature measurement;

[0065] The temperature difference value monitored in the area refers to the difference between the temperature values ​​monitored by multiple sensors in the preset area. Its acquisition logic is to collect the temperature data of the area in real time by acquiring each sensor in the area, and to obtain the temperature difference value monitored in the area by subtracting the maximum temperature collected by the sensors in the area from the minimum temperature. ;

[0066] There is no limitation on the number and installation positions of sensors in the preset area. The experimenter can understand that after the preset area is set by the above method, the monitoring points of the sensors are divided according to the heat conduction and convection characteristics, etc., which will not be elaborated here.

[0067] The regional thermal imbalance index refers to an indicator used to describe the degree of uneven temperature distribution in a certain area. It usually reflects the degree of temperature difference between different locations in the area. Its acquisition logic is to collect temperature data of multiple monitoring points in a preset area in real time through sensors, and then calculate the maximum and minimum temperature difference in the area. The standard deviation reflects the degree of temperature difference in the area, and substitutes the weighted average to obtain the regional thermal imbalance index. ;

[0068] Specifically, the calculation formulas for the maximum-minimum temperature difference and the standard deviation are prior art and will not be described in detail here;

[0069] The regional temperature mean refers to the average temperature measured by all sensors in the preset area. The acquisition logic is to collect temperature data, accumulate temperature data and calculate the ratio of the temperature data to obtain the regional temperature mean. ;

[0070] S2: According to the importance coefficient of thermistor and high temperature resistant sensor, the difference of monitored temperature difference in the region, the regional thermal imbalance index and the regional temperature mean, a data analysis model is established to generate the regional evaluation coefficient;

[0071] Among them, the data analysis model refers to a weighted analysis model, which generates regional assessment coefficients through weighted calculation;

[0072] Obtain the importance coefficient of thermistor and high temperature resistant sensor, the difference of monitored temperature difference within the region, the regional thermal imbalance index and the regional temperature mean to establish a data analysis model and generate the regional evaluation coefficient , based on the formula:

[0073] ;

[0074] In the formula, is the regional assessment coefficient, , , as well as They are the importance coefficients of thermistor and high temperature resistant sensor , monitor the temperature difference within the area , Regional Thermal Imbalance Index And the regional temperature mean The preset scaling factor of , , as well as All are greater than 0;

[0075] Among them, the importance coefficient of thermistor and high temperature resistant sensor, the difference of monitored temperature difference in the region, the regional thermal imbalance index and the regional temperature mean are all data manifestations that directly express the adjustment range of the current preset region;

[0076] It can be seen from the formula that the higher the importance coefficient of thermistor and high temperature resistant sensor, the difference of monitored temperature difference in the area, and the regional thermal imbalance index, the more the range of the current preset area needs to be adjusted, and the higher the regional evaluation coefficient. On the contrary, the higher the regional temperature average, the more stable the range of the current preset area is, and the lower the regional evaluation coefficient is.

[0077] S3: Obtaining a regional evaluation coefficient and comparing it with a preset regional threshold to obtain a comparison result, determining the preset region that needs to be adjusted according to the comparison result, and then counting the number of preset regions that need to be adjusted and the corresponding regional evaluation coefficients and the number of preset regions that do not need to be adjusted and the corresponding regional evaluation coefficients to obtain a regional adjustment result;

[0078] The logic of obtaining the regional threshold is to collect a set of historical sensor screening solutions, divide the data set into a training set and a test set, set the evaluation index and clustering algorithm, and in each round of cross-validation, train the model on the training set and evaluate the model performance on the test set. Then, adjust the regional threshold according to the performance of the validation set. Therefore, the regional threshold is constantly updated.

[0079] In the present invention, clustering algorithm is a kind of unsupervised learning algorithm, which is used to divide the regional data in the data set into marked groups or clusters; the common one is K-means clustering, which divides the weighing data in the data set into K clusters, so that the distance between each regional data and the center point (center of mass) of the cluster to which it belongs is minimized, and finally the adjustment range of the regional data is measured by the Euclidean distance, so as to set the regional threshold;

[0080] After obtaining the regional assessment coefficient, the regional assessment coefficient is compared and analyzed with the continuously iterated regional threshold;

[0081] If the regional evaluation coefficient is greater than or equal to the regional threshold, the current preset region is marked as a preset region that needs to be adjusted, and an adjustment signal is generated;

[0082] If the regional evaluation coefficient is less than the regional threshold, the current preset region is marked as a preset region that does not need to be adjusted, and a stable signal is generated;

[0083] The number of preset areas marked as needing adjustment and the corresponding regional evaluation coefficients are counted, and the preset areas marked as not needing adjustment and the corresponding regional evaluation coefficients are obtained and accumulated and calculated. The specific expression is: , where n is the number of preset areas that need to be adjusted, , where m is the number of preset areas that do not need to be adjusted;

[0084] The accumulated calculation results are calculated by weighted ratio to obtain the preset area adjustment range. The specific formula is as follows:

[0085] ;

[0086] In the formula, Adjust the amplitude for the preset area. and The weights of the calculated results are respectively added for the preset areas marked as needing adjustment and the preset areas marked as not needing adjustment;

[0087] According to the preset area adjustment range, a reduction range of the preset area to be adjusted is determined to obtain an area adjustment result;

[0088] The present invention establishes a data analysis model by collecting the importance coefficient of thermistor and high-temperature resistant sensors, the monitored temperature difference value within the area, the regional thermal imbalance index and the regional temperature mean, generates a regional evaluation coefficient, and compares it with the preset regional threshold to obtain a comparison result, determines the preset area that needs to be adjusted according to the comparison result, and then counts the number of preset areas that need to be adjusted and the corresponding regional evaluation coefficients and the number of preset areas that do not need to be adjusted and the corresponding regional evaluation coefficients to obtain the regional adjustment result, accurately evaluates the regional temperature control state and the monitoring accuracy, dynamically adjusts the regional range, and improves temperature uniformity.

[0089] Example 2

[0090] In Example 1 of the present invention, examples are given to illustrate the collection of importance coefficients of thermistor-resistant high-temperature sensors, monitored temperature differences within the region, regional thermal imbalance indexes, and regional temperature averages, the establishment of a data analysis model, the generation of regional evaluation coefficients, and comparisons with preset regional thresholds to obtain comparison results, and the preset regions that need to be adjusted are determined based on the comparison results, and then the number of preset regions that need to be adjusted and the corresponding regional evaluation coefficients and the number of preset regions that do not need to be adjusted and the corresponding regional evaluation coefficients are counted to obtain an operation strategy for regional adjustment results; however, in Example 1, only regional adjustment is used to improve the monitoring accuracy of regional temperature control. However, the temperature control system of the furnace will supplement heat energy based on uneven heat distribution. Blindly supplementing heat energy ignores the conditions of heat conduction and convection, resulting in excess supplementary heat energy, which wastes resources, affects grain growth, and reduces crystallinity; in response to the above problems, Example 2 of the present invention is further refined;

[0091] S4: Obtaining the regional adjustment results, counting the adjusted preset areas, and comparing them with the adjustment thresholds. Based on the comparison results, determining to conduct power distribution forecasting analysis through a time series model, and further refining and optimizing the heat energy supplement distribution plan;

[0092] The regional adjustment result includes the adjusted preset regions and their quantities;

[0093] The number of adjusted preset areas is compared with the adjustment threshold. If the number of adjusted preset areas is greater than or equal to the adjustment threshold, it means that there is a heat energy supplement range in the currently adjusted area. Otherwise, it means that the temperature in the currently adjusted area is within the normal range, and an end signal is generated;

[0094] The adjustment threshold is set based on the historical regional assessment coefficient and the number of preset regions that have been adjusted, which will not be elaborated here;

[0095] It should be noted that the time series model of this embodiment adopts the ARIMAX model. Refining and optimizing the heat energy supplement allocation plan means that when it is determined that there is a heat energy supplement range in the currently adjusted area, through further analysis and adjustment, the heat energy supplement allocation plan is made more scientific and reasonable, thereby realizing the control of the temperature of all areas inside the furnace;

[0096] Furthermore, the specific steps of heat energy distribution forecasting analysis through time series model are as follows:

[0097] Step A1, obtaining prediction data;

[0098] Step A2, establishing an ARIMAX model;

[0099] Step A3, using the maximum likelihood estimation (MLE) method to estimate the ARIMAX model parameters;

[0100] Step A4, verify the effect of the fitting model and check the goodness of fit of the model by residual analysis;

[0101] Step A5, using the fitted model to predict the future heat energy supplement allocation plan, and taking the maximum value of the predicted result as the highest point of heat energy supplement in the future time period.

[0102] Specifically, the prediction data includes the importance coefficient of thermistor-resistant high-temperature sensors corresponding to each time stamp, the monitored temperature difference value in the region, the regional thermal imbalance index, the regional temperature mean, and the thermal energy supplement data; wherein, the importance coefficient of thermistor-resistant high-temperature sensors corresponding to each time point, the monitored temperature difference value in the region, the regional thermal imbalance index, and the regional temperature mean have been exemplified in Example 1 and will not be repeated here;

[0103] Heat replenishment data refers to historical heat replenishment data, and its data is used as the main variable of the time series;

[0104] Furthermore, the basic form of the ARIMAX model is:

[0105] ;

[0106] In the formula, is the heat energy supplement value at the current timestamp, is the value of the heat energy replenishment value at the current timestamp after i time units, is a constant term, is the i-th order autoregressive parameter, is the order of the autoregressive term, is the j-th order moving average parameter, is the order of the moving average term, is the white noise term, is the value of the white noise term after the lag of j time units, representing the random error, is an exogenous variable The coefficient of is the lag order of the exogenous variable, is an exogenous variable lagged by k periods.

[0107] It should be noted that the exogenous variable part can integrate the influence of other relevant variables (such as the importance coefficient of thermistor and high temperature resistant sensor, the monitored temperature difference value in the region, the regional thermal imbalance index, the regional temperature mean, the thermal conduction rate in the thermal energy replenishment range, and the thermal convection rate in the thermal energy replenishment range) on the thermal energy replenishment;

[0108] Among them, the heat conduction rate in the heat energy supplement range and the heat convection rate in the heat energy supplement range are both obtained through existing technologies and will not be described in detail here;

[0109] It should be noted that in step A3, , , , , It is calculated by the maximum likelihood estimation (MLE) method. The specific steps are as follows:

[0110] Error term Normal distribution , then the likelihood function is:

[0111] ;

[0112] Taking the logarithm of the likelihood function gives the log-likelihood function:

[0113] ;

[0114] By maximizing the log-likelihood function, we can obtain the parameter estimates , , , , ;

[0115] Dynamically adjust the heat energy supplement allocation strategy by predicting the maximum, minimum and average values ​​of the results;

[0116] This embodiment exemplifies as follows:

[0117] The maximum value of the predicted result It represents the highest point of heat replenishment in the future time period. A peak allocation strategy is formulated to allocate heat during the predicted peak heat replenishment period (i.e. close to or equal to At the time point of the experiment, the heat output power of the furnace was increased by 13% to ensure timely heat replenishment to meet the preparation needs;

[0118] According to the lowest value of the predicted results or valley interval, formulate a valley allocation strategy, and in the valley period of heat energy replenishment (i.e. close to or below At the time point of the experiment, the heat output power of the furnace was reduced by 18% to ensure that the heat energy was not overcharged, stabilize the grain growth, and improve the crystallinity;

[0119] According to the average of the predicted results , formulate a balance period allocation strategy to maintain heat output during the period when heat replenishment is close to the average value to ensure continuous and stable operation of the furnace;

[0120] The peak period, balance period and trough period are set by the experimenters, and the boundaries of the division are not limited.

[0121] The present invention obtains the regional adjustment results, counts the adjusted preset areas, and compares them with the adjustment thresholds. Based on the comparison results, it determines to perform power distribution forecast analysis through a time series model, further refines and optimizes the heat energy supplement distribution plan, avoids excessive heat energy supplementation, saves resources, ensures normal grain growth, and improves crystallinity.

[0122] During the preparation of the battery cell, the positive electrode material needs to be evenly coated on the aluminum foil current collector, and the negative electrode material needs to be coated on the copper foil current collector, and then compacted after drying to ensure uniform thickness; then the positive electrode sheet, negative electrode sheet and separator are assembled using a winding or stacking process, and the electrolyte is injected into the battery cell by vacuum injection to ensure uniform electrolyte distribution; finally, thermistors or thin-film thermocouple sensors are embedded in key areas inside the battery cell (such as the contact points between the positive and negative electrodes), and the sensors are sealed with electrolyte-resistant polytetrafluoroethylene packaging materials to prevent leakage, and the sensor wires are led out of the battery cell and connected to the external control circuit through a flexible circuit;

[0123] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0124] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0125] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0126] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0127] Figure 2 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 2 As shown, the electronic device 2 of this embodiment includes: at least one processor 20 ( Figure 2 Only one is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on at least one processor 20, the processor 20 executes the computer program 22 to implement the above Figure 1 Steps in a method embodiment.

[0128] The electronic device 2 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 2 may include but is not limited to a processor 20 and a memory 21. Those skilled in the art will appreciate that Figure 2 It is only an example of the electronic device 2 and does not constitute a limitation on the electronic device 2. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0129] The processor 20 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0130] In some embodiments, the memory 21 may be an internal storage unit of the electronic device 2, such as a hard disk or memory of the electronic device 2. In other embodiments, the memory 21 may also be an external storage device of the electronic device 2, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 2. Further, the memory 21 may also include both an internal storage unit and an external storage device of the electronic device 2. The memory 21 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of a computer program. The memory 21 may also be used to temporarily store data that has been output or is to be output.

[0131] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0132] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0133] A computer-readable storage medium provided in an embodiment of the present application has the same beneficial effects as the above-mentioned sodium ion battery preparation method.

[0134] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0135] A computer program product provided in an embodiment of the present application has the same beneficial effects as the above-mentioned sodium ion battery preparation method.

[0136] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0137] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0138] In the embodiments provided in the present application, it should be understood that the disclosed devices / equipment and methods can be implemented in other ways. For example, the device / equipment embodiments described above are merely schematic, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for preparing a sodium ion battery with integrated temperature monitoring, characterized in that: Including S1: collecting regional monitoring feature information and regional temperature change characteristics, and obtaining the importance coefficient of thermistor and high temperature resistant sensor, the monitoring temperature difference value in the region, the regional thermal imbalance index and the regional temperature mean through normalization processing; S2: According to the importance coefficient of thermistor and high temperature resistant sensor, the monitored temperature difference value in the region, the regional thermal imbalance index and the regional temperature mean, a data analysis model is established to generate the regional evaluation coefficient; S3: Obtaining a regional evaluation coefficient and comparing it with a preset regional threshold to obtain a comparison result, determining the preset region that needs to be adjusted according to the comparison result, and then counting the number of preset regions that need to be adjusted and the corresponding regional evaluation coefficients and the number of preset regions that do not need to be adjusted and the corresponding regional evaluation coefficients to obtain a regional adjustment result; S4: Obtain the regional adjustment results, count the number of preset regions that have been adjusted, and compare them with the adjustment threshold. Based on the comparison results, determine to perform heat energy distribution forecast analysis through the time series model, and further refine and optimize the heat energy supplement distribution plan; Regional monitoring characteristic information includes the importance coefficient of thermistor and the monitored temperature difference in the region; regional temperature change characteristics include regional thermal imbalance index and regional temperature mean; The importance coefficient of the thermistor is obtained by weighted calculation based on the temperature standard deviation of the timestamp in the area corresponding to the sensor, the response time of the sensor, and the difference between the temperature value monitored by the sensor and the temperature control reaction temperature of the furnace. ; Where i is the i-th timestamp and g is the g-th preset area; The temperature data of the area is collected by each sensor in the area in real time, and the maximum temperature collected by the sensor in the area is subtracted from the minimum temperature to obtain the monitored temperature difference value in the area. ; The temperature data of multiple monitoring points in the preset area are collected in real time by sensors, and then the maximum and minimum temperature difference in the area are calculated. The standard deviation reflects the degree of temperature difference in the area, and the regional thermal imbalance index is obtained by substituting the weighted average. ; Through temperature data collection, the temperature data is accumulated and the ratio is calculated by the number of temperature data to obtain the regional temperature average. .

2. The method for preparing a sodium ion battery with integrated temperature monitoring according to claim 1, characterized in that: Obtain the importance coefficient of thermistor and high temperature resistant sensor, the monitored temperature difference value within the region, the regional thermal imbalance index and the regional temperature mean to establish a data analysis model and generate the regional evaluation coefficient , based on the formula: ； In the formula, is the regional assessment coefficient, , , as well as They are the importance coefficients of thermistor and high temperature resistant sensor , monitor the temperature difference in the area , Regional Thermal Imbalance Index And the regional temperature mean The preset scaling factor of , , as well as Both are greater than 0.

3. The method for preparing a sodium ion battery with integrated temperature monitoring according to claim 2, characterized in that: After obtaining the regional assessment coefficient, the regional assessment coefficient is compared and analyzed with the preset regional threshold; If the regional evaluation coefficient is greater than or equal to the preset regional threshold, the current preset region is marked as a preset region that needs to be adjusted, and an adjustment signal is generated; If the regional evaluation coefficient is less than the preset regional threshold, the current preset region is marked as a preset region that does not need to be adjusted, and a stable signal is generated.

4. The method for preparing a sodium ion battery with integrated temperature monitoring according to claim 3, characterized in that: The number of preset areas marked as needing adjustment and the corresponding regional evaluation coefficients are counted, and the preset areas marked as not needing adjustment and the corresponding regional evaluation coefficients are obtained and accumulated and calculated. The specific expression is: , where n is the number of preset areas that need to be adjusted, , where m is the number of preset areas that do not need to be adjusted; the accumulated calculation results are weighted ratio calculated to obtain the preset area adjustment range. The specific formula is as follows: ； In the formula, Adjust the amplitude for the preset area. and The weights of the calculated results are respectively added for the preset areas marked as needing adjustment and the preset areas marked as not needing adjustment; According to the preset area adjustment range, the reduction range of the preset area to be adjusted is determined to obtain the area adjustment result.

5. The method for preparing a sodium ion battery with integrated temperature monitoring according to claim 4, characterized in that: The time series model uses the ARIMAX model. The specific steps for heat energy distribution forecasting and analysis using the time series model are as follows: Step A1, obtaining prediction data; Step A2, establishing an ARIMAX model; Step A3, using the maximum likelihood estimation method to estimate the ARIMAX model parameters; Step A4, verify the effect of the fitting model and check the goodness of fit of the model by residual analysis; Step A5, using the fitted model to predict the future heat energy supplement allocation plan, and taking the maximum value of the predicted result as the highest point of heat energy supplement in the future time period.

6. The method for preparing a sodium ion battery with integrated temperature monitoring according to claim 5, characterized in that: The exogenous variables in the ARIMAX model include the importance coefficient of thermistor and high temperature resistant sensor, the monitored temperature difference within the region, the regional thermal imbalance index, the regional temperature mean, the heat conduction rate within the heat energy supplement range, and the heat convection rate within the heat energy supplement range. The ARIMAX model formula is: ； In the formula, is the heat energy supplement value at the current timestamp, is the value of the heat energy replenishment value at the current timestamp after i time units, is a constant term, is the i-th order autoregressive parameter, is the order of the autoregressive term, is the j-th order moving average parameter, is the order of the moving average term, is the white noise term, is the value of the white noise term after the lag of j time units, representing the random error, is an exogenous variable The coefficient of is the lag order of the exogenous variable, is an exogenous variable lagged by k periods.

7. The method for preparing a sodium ion battery with integrated temperature monitoring according to claim 6, characterized in that: In step A3, , , , , It is calculated by the maximum likelihood estimation method.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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