Energy storage battery thermal management method and system

Abstract

This invention relates to the field of battery thermal management technology, specifically disclosing a thermal management method and system for energy storage batteries. During the charging process of the energy storage battery, a charging monitoring cycle is set to identify the final stage of full charge. Determining this final stage allows for precise determination of the start time for thermal management operations such as temperature monitoring, avoiding high-frequency temperature monitoring throughout the entire charging process and reducing unnecessary monitoring operations. Temperature monitoring and analysis of the battery cells during the final stage of full charge are performed, and the degree of latent localized overheating during this stage is assessed. Assessing latent localized overheating can detect potential thermal runaway risks in advance and improve the accuracy of identifying abnormal temperature areas within the battery pack.

Description

Technical Field

[0001] This invention relates to the field of battery thermal management technology, and specifically to a method and system for thermal management of energy storage batteries. Background Technology

[0002] With the increasing global demand for clean energy, energy storage technology, as a key support for energy transition, is playing an increasingly important role in areas such as power system peak shaving, renewable energy consumption, and distributed energy systems. Among these technologies, energy storage batteries, with their advantages of high energy density and long cycle life, have become one of the core devices in the energy storage field.

[0003] During the charging process of energy storage batteries, the heat generation characteristics differ at different stages. Towards the end of a full charge, the internal chemical reactions of the battery are nearly complete, making the heat generation situation more complex and critical. On the one hand, due to the transformation of internal materials and changes in electrode structure, localized heat generation may increase sharply, easily forming hidden localized overheating areas. Most existing thermal management technologies focus on macroscopic temperature monitoring and control of the entire battery pack, making it difficult to accurately locate these hidden localized overheating areas. This leads to the inability to detect potential thermal runaway risks in a timely manner, thus affecting the accuracy of determining abnormal temperature areas within the battery pack.

[0004] On the other hand, towards the end of a full charge, sudden changes in battery heat generation may occur, and battery thermal inertia also affects temperature changes. Battery thermal inertia refers to the phenomenon that, due to the characteristics of its materials and structure, temperature changes do not immediately follow changes in heat generation during a battery's temperature fluctuations. Existing thermal management methods, when faced with sudden changes in heat generation and thermal inertia, use a fixed temperature sampling frequency that remains unchanged throughout the charging process. This fixed sampling method does not take into account the differences in battery thermal characteristics at different stages. In the complex thermal change phase towards the end of a full charge, a fixed low sampling frequency may not meet the needs for accurate temperature monitoring, while a fixed high sampling frequency will increase system resource consumption and costs.

[0005] Therefore, the present invention provides a thermal management method and system for energy storage batteries. Summary of the Invention

[0006] The purpose of this invention is to provide a thermal management method and system for energy storage batteries to solve the aforementioned background problems.

[0007] The objective of this invention can be achieved through the following technical solutions: A thermal management method for an energy storage battery, comprising: During the charging process of the energy storage battery, an energy storage charging monitoring cycle is set to identify the end period of full charge and determine the end period of full charge. Temperature monitoring and analysis of battery cells during the final stage of full charge of energy storage batteries are conducted, and the degree of hidden local overheating of energy storage batteries during the final stage of full charge is evaluated. When the assessment result of the degree of latent local overheating is high local overheating, the heat generation mutation of the battery cell temperature change is identified, and it is determined whether battery thermal inertia occurs. When a sudden change in thermal inertia signal occurs, determine the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia. If a low frequency matching signal occurs, obtain the battery temperature sampling frequency adjustment amount, and manage and adjust the current battery temperature sampling frequency according to the battery temperature sampling frequency adjustment amount.

[0008] As a further aspect of the present invention, the process of identification during the final stage of a full charge of the energy storage battery is as follows: Each energy storage charging monitoring cycle is equally divided into several energy storage charging monitoring points. The current total charging voltage of the energy storage battery at each energy storage charging monitoring point is obtained, and a total charging voltage change curve is constructed. A charging cutoff voltage line parallel to the X-axis is drawn on the total charging voltage change curve. The difference between the current total charging voltage and the charging cutoff voltage within each voltage analysis pair is taken as the absolute value to obtain the charging cutoff analysis value. Obtain the charging cutoff analysis value within the charging cutoff interval, extract the X-coordinate corresponding to the charging cutoff analysis value on the total charging voltage change curve, and compare the extracted X-coordinate before and after time. Select the X-coordinate at the beginning and the X-coordinate at the end of time to determine the suspected full charging period.

[0009] As a further aspect of the present invention, the process for determining the final stage of a full charge is as follows: The mean and standard deviation of the current total charging voltage during the suspected full charging period are calculated separately to obtain the mean and standard deviation of the current total charging voltage. Substitute the current total charging voltage standard deviation and the current total charging voltage mean into the coefficient of variation formula to output the suspected full charge voltage analysis value. If the suspected full charge voltage analysis value is less than or equal to the suspected full charge voltage analysis threshold, it is the end of the full charge period.

[0010] As a further aspect of the present invention, the process of monitoring and analyzing the temperature of the battery cells during the final stage of a full charge of an energy storage battery is as follows: Arbitrarily select a battery cell as the reference cell, and the battery cells adjacent to the reference cell as the neighboring cells. The area constructed by the reference cell and all the neighboring cells is used as the cell temperature analysis area. Obtain the cell temperature value of the reference cell at the energy storage charging monitoring point, and calculate the difference between the reference cell and the cell temperature value of each neighboring cell at the energy storage charging monitoring point. Take the absolute value and then sum and average the values ​​to obtain the area temperature difference analysis value. If the regional temperature difference analysis value is greater than the regional temperature difference analysis threshold, the analyzed cell temperature analysis area will be marked as the cell abnormal temperature area, and the corresponding energy storage charging monitoring point will be marked as the cell abnormal temperature monitoring point.

[0011] As a further aspect of the present invention, the evaluation process for the degree of latent local overheating is as follows: The number of continuous abnormal temperature monitoring points in the cell abnormal temperature zone during the end of the full charge period is counted, and the proportion of this number to the total number of energy storage charging monitoring points during the end of the full charge period is used to obtain the regional abnormal temperature duration value. After summing and averaging the regional temperature difference analysis values ​​corresponding to the continuous cell temperature monitoring points, the ratio of the average value to the cell calibration temperature is calculated to obtain the regional temperature difference degree value. The local overheating analysis value is obtained by summing the regional temperature variation duration value and the regional temperature variation degree value. If the local overheating analysis value is greater than the local overheating analysis threshold, it is displayed as a high local overheating signal.

[0012] As a further aspect of the present invention, the process for identifying sudden thermal changes in battery cell temperature is as follows: The reference cell in the cell temperature range is combined with each adjacent cell to obtain multiple cell temperature analysis pairs. The temperature difference between the reference cell and the adjacent cells at the same energy storage charging monitoring point during the end of the full charge period is obtained. The absolute value is taken to obtain the temperature difference value of a single pair of cells. The temperature difference between adjacent pairs of cells during the final stage of full charge is calculated to obtain the cell temperature difference gradient value. Extract the cell temperature gradient values ​​that are positive and greater than the cell temperature gradient threshold, sort them according to the time series, integrate the continuous cell temperature gradient values ​​in the time series, and calculate the summation and mean to obtain the unit temperature difference mutation analysis value.

[0013] As a further aspect of the present invention, the process for determining the occurrence of battery thermal inertia is as follows: The mean and standard deviation of the unit temperature difference mutation analysis values ​​during the end of the full charge period were calculated separately to obtain the mean and standard deviation of the unit temperature difference mutation analysis. The suspected temperature difference mutation analysis values ​​were then obtained by using the coefficient of variation formula. If the suspected temperature difference mutation analysis value is greater than the suspected temperature difference mutation analysis threshold, it will be displayed as a heat generation mutation thermal inertia signal.

[0014] As a further aspect of the present invention, the analysis process for the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia is as follows: Extract all current battery temperature monitoring points during the final stage of full charge, and the current temperature sampling value corresponding to each current battery temperature monitoring point. Compare the current temperature sampling value corresponding to each current battery temperature monitoring point with the cell temperature value of the reference cell at the energy storage charging monitoring point. The current battery temperature monitoring point corresponding to the current temperature sampling value that coincides with the cell temperature value of the reference cell at the energy storage charging monitoring point is taken as the current sampling temperature point. The time interval between the energy storage charging monitoring point and the current sampling temperature point is obtained, and the proportion of the time interval between them to the end of the full charge period is used as the sampling interval ratio.

[0015] As a further aspect of the present invention, the process of acquiring the battery temperature sampling frequency adjustment amount and managing and adjusting the current battery temperature sampling frequency is as follows: The average of all sampling interval ratios is calculated to obtain the sampling interval analysis value. If the sampling interval analysis value is greater than the sampling interval analysis threshold, it is displayed as a low-degree sampling frequency matching signal. The average of the maximum and minimum sampling interval ratios is calculated and then the reciprocal is processed to obtain the electric temperature sampling frequency adjustment amount. The current battery temperature sampling frequency is summed with the battery temperature sampling frequency adjustment amount to obtain the battery temperature sampling frequency management adjustment value.

[0016] An energy storage battery thermal management system includes: Time Period Determination Module: During the charging process of the energy storage battery, the energy storage charging monitoring cycle is set, and the end period of full charge of the energy storage battery is identified and determined. Overheat assessment module: Monitors and analyzes the temperature of battery cells during the final stage of a full charge of the energy storage battery, and assesses the degree of hidden local overheating of the energy storage battery during the final stage of a full charge. Thermal mutation analysis module: When the assessment result of the degree of latent local overheating is high local overheating, the module identifies the thermal mutation of the battery cell temperature change and determines whether battery thermal inertia has occurred. Management and Adjustment Module: When a sudden change in thermal inertia signal occurs, determine the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia. If a low frequency matching signal occurs, obtain the battery temperature sampling frequency adjustment amount and perform management and adjustment operations on the current battery temperature sampling frequency according to the battery temperature sampling frequency adjustment amount.

[0017] The beneficial effects of this invention are as follows: 1. This invention sets a charging monitoring cycle during the charging process of an energy storage battery, identifies the final stage of full charge, and accurately determines the start time of thermal management operations such as temperature monitoring. This avoids high-frequency temperature monitoring throughout the entire charging process, reducing unnecessary monitoring operations. Temperature monitoring and analysis of the battery cells during the final stage of full charge allows for the assessment of latent local overheating. Assessing latent local overheating can detect potential thermal runaway risks in advance and improves the accuracy of identifying abnormal temperature areas within the battery pack.

[0018] 2. When the assessment result of the latent local overheating degree is high local overheating, the present invention identifies the heat generation mutation of the battery cell temperature change and determines whether battery thermal inertia occurs. When a heat generation mutation thermal inertia signal occurs, the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia is determined. If a low sampling frequency matching signal occurs, the battery temperature sampling frequency adjustment amount is obtained, and the current battery temperature sampling frequency is managed and adjusted according to the battery temperature sampling frequency adjustment amount. This can record rapid temperature changes in a timely manner, reduce temperature data errors caused by improper sampling frequency, and achieve the purpose of dynamic temperature control for energy storage battery thermal management. Attached Figure Description

[0019] The invention will now be further described with reference to the accompanying drawings.

[0020] Figure 1 This is a flowchart of the steps of a thermal management method for an energy storage battery according to the present invention; Figure 2 This is a flowchart illustrating the determination process of a thermal management method for energy storage batteries according to the present invention. Figure 3 This is a flowchart of a module of an energy storage battery thermal management system according to the present invention. Detailed Implementation

[0021] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0022] Example 1 like Figure 1 - Figure 2As shown, during the daily charging process of energy storage batteries, when the battery is nearing full charge, the electrochemical polarization inside the battery suddenly increases, causing a sudden change in the heat generation pattern. The temperature rise rate of the battery cells is faster than that of the battery surface, resulting in thermal inertia. This causes the temperature sampling frequency during the full charge phase to lag behind the thermal inertia, ultimately leading to uneven heat generation due to battery polarization within the battery pack. This widens the temperature difference between individual battery cells, and the overheated areas within the battery pack expand over time. Therefore, this invention provides a thermal management method for energy storage batteries, including: Step 1: During the charging process of the energy storage battery, set the energy storage charging monitoring cycle, identify the end period of full charge, and determine the end period of full charge. In some embodiments, each energy storage charging monitoring cycle is equally divided into several energy storage charging monitoring points, wherein the time interval between adjacent energy storage charging monitoring points is of equal length; The current total charging voltage of the energy storage battery at each energy storage charging monitoring point is obtained and substituted into a two-dimensional coordinate system, with the X-axis representing time and the Y-axis representing the total charging voltage, to construct a curve of the total charging voltage change. Mark the charging cutoff voltage point on the Y-axis of the total charging voltage change curve, and draw a straight line parallel to the X-axis starting from the charging cutoff voltage point as the charging cutoff voltage line. Extract all Y coordinates from the total charging voltage change curve, and combine them with the charging cutoff voltage according to the time series of the relative X-axis coordinates to obtain multiple voltage analysis pairs; The difference between the current total charging voltage and the charging cutoff voltage within each voltage analysis pair is taken as the absolute value to obtain the charging cutoff analysis value. Obtain the charging cut-off analysis value within the charging cut-off interval, extract the X-coordinate corresponding to the charging cut-off analysis value on the total charging voltage change curve, and compare the extracted X-coordinate before and after time. Select the X-coordinate at the beginning of time and the X-coordinate at the end of time to determine the suspected full charging period. It should be noted that the charging cutoff interval refers to the time range during the charging process of an energy storage battery when the current total charging voltage and the charging cutoff voltage are close to a certain standard (reflected by the charging cutoff analysis value). Within this range, the battery may be in the final stage of full charging, providing a key basis for subsequently determining the suspected full charging period. The mean and standard deviation of the current total charging voltage during the suspected full charging period are calculated separately to obtain the mean and standard deviation of the current total charging voltage. Substitute the current standard deviation of the total charging voltage and the current mean of the total charging voltage into the coefficient of variation formula to output the suspected full-charge voltage analysis value. It is understandable that the meaning of the suspected full charge voltage analysis value is: to reflect the relative relationship between the dispersion and central tendency of the total charging voltage during the suspected full charge period. Specifically, if the suspected full charge voltage analysis value is larger, it means that the total charging voltage fluctuates relatively more during the analyzed suspected full charge period, the voltage fluctuates more, and the charging state of the battery fluctuates. If the suspected full charge voltage analysis value is smaller, it means that the total charging voltage fluctuates relatively less during the analyzed suspected full charge period, the voltage is more stable, and the charging state of the battery is stable. If the suspected full charge voltage analysis value is greater than the suspected full charge voltage analysis threshold, it indicates that the total charging voltage fluctuates relatively much during the analyzed suspected full charge period, the voltage fluctuates, the battery charging state fluctuates, and the analyzed suspected full charge period is a non-full charge end period. If the suspected full charge voltage analysis value is less than or equal to the suspected full charge voltage analysis threshold, it means that the fluctuation of the total charging voltage is relatively small and the voltage is relatively stable during the analyzed suspected full charge period. The battery charging state is stable, and the analyzed suspected full charge period is the end of the full charge period. Step 2: Perform temperature monitoring and analysis on the battery cells during the final stage of full charge of the energy storage battery, and evaluate the degree of hidden local overheating of the energy storage battery during the final stage of full charge. In some embodiments, the cell temperature value at each energy storage charging monitoring point during the end of the full charge period is obtained, an arbitrary battery cell is selected as the reference cell, the battery cells adjacent to the reference cell are taken as the base neighbor cells, and the area constructed by the reference cell and all base neighbor cells is taken as the cell temperature analysis area. It should be noted that the base cell refers to the battery cell that is adjacent to the reference cell in the directions above, below, to the left, to the right, and diagonally. The temperature difference between the reference cell at the energy storage charging monitoring point and the temperature difference between each adjacent cell at the energy storage charging monitoring point is calculated. The absolute values ​​are then summed and averaged to obtain the regional temperature difference analysis value. It should be noted that the energy storage charging monitoring point corresponding to the reference cell and the energy storage charging monitoring point corresponding to the adjacent cell are the same energy storage charging monitoring point. It is understandable that the regional temperature difference analysis value represents an index used to quantify the degree of temperature difference in the cell temperature analysis area during the final stage of a full charge of an energy storage battery. It reflects the uniformity of temperature within the cell temperature analysis area. Specifically, the smaller the regional temperature difference analysis value, the smaller the temperature difference between the reference cell and each of its neighboring cells, the more uniform the temperature distribution within the cell temperature analysis area, and the more balanced the heat transfer of the battery cells at the end of a full charge, with no obvious local temperature anomalies. Conversely, the larger the regional temperature difference analysis value, the larger the temperature difference between the reference cell and each of its neighboring cells, the less uniform the temperature distribution within the cell temperature analysis area, and the more unbalanced the heat transfer of the battery cells at the end of a full charge, with obvious local temperature anomalies. If the temperature difference analysis value of the region is greater than the temperature difference analysis threshold of the region, it indicates that the temperature difference between the reference cell and each adjacent cell is large, the temperature distribution in the cell temperature analysis area is uneven, the heat transfer of the battery cell is unbalanced at the end of the full charge, and obvious local temperature anomalies occur. The analyzed cell temperature analysis area is marked as the cell abnormal temperature area, and the corresponding energy storage charging monitoring point is marked as the cell abnormal temperature monitoring point. If the temperature difference analysis value of the region is less than or equal to the temperature difference analysis threshold of the region, it means that the temperature difference between the reference cell and each adjacent cell is small, the temperature distribution in the cell temperature analysis area is relatively uniform, the heat transfer of the battery cell is relatively balanced at the end of the full charge, and there is no obvious local temperature anomaly. The analyzed cell temperature analysis area is marked as the cell non-differential temperature area. The number of continuous abnormal temperature monitoring points in the cell abnormal temperature zone during the end of the full charge period is counted, and the proportion of this number to the total number of energy storage charging monitoring points during the end of the full charge period is used to obtain the regional abnormal temperature duration value. After summing and averaging the regional temperature difference analysis values ​​corresponding to the continuous cell temperature monitoring points, the ratio of the average value to the cell calibration temperature is calculated to obtain the regional temperature difference degree value. The local overheating analysis value is obtained by summing the regional anomaly duration value and the regional anomaly degree value. It is understandable that the meaning of the local overheating analysis value is: from the two key dimensions of the duration and degree of temperature anomaly, the overall situation of hidden local overheating of battery cell is comprehensively evaluated. Specifically, the regional temperature anomaly duration value reflects the duration of local temperature anomaly of battery cell in the time dimension, and the regional temperature anomaly degree value reflects the degree of temperature difference within the temperature analysis area of ​​battery cell. If the local overheating analysis value is greater than the local overheating analysis threshold, it indicates that the temperature difference within the cell temperature analysis area is large and the temperature anomaly lasts for a long time, which is a high local overheating signal. If the local overheating analysis value is less than or equal to the local overheating analysis threshold, it indicates that the temperature difference within the cell temperature analysis area is small and the duration of the temperature anomaly in the time dimension is short, which is displayed as a low-degree local overheating signal.

[0023] The specific solution in this embodiment is as follows: During the charging process of the energy storage battery, an energy storage charging monitoring cycle is set to identify the end period of full charge. Determining the end period of full charge can accurately determine the start time of thermal management operations such as temperature monitoring, avoiding high-frequency temperature monitoring throughout the entire charging process and reducing unnecessary monitoring operations. Temperature monitoring and analysis are performed on the battery cells during the end period of full charge, and the degree of latent local overheating of the energy storage battery during the end period of full charge is evaluated. Assessing the degree of latent local overheating can detect potential thermal runaway risks in advance and improve the accuracy of identifying abnormal temperature areas within the battery pack.

[0024] Example 2 like Figure 1 - Figure 2 As shown, this embodiment of the invention provides a thermal management method for energy storage batteries, which further includes: Step 3: When the assessment result of the degree of latent local overheating is high local overheating, the heat generation mutation of the battery cell temperature change is identified, and it is determined whether battery thermal inertia has occurred. In some embodiments, a reference cell within the cell temperature range is combined with each adjacent cell to obtain multiple cell temperature analysis pairs; For example, within the cell temperature analysis pair, the temperature difference between the cell temperature values ​​of the reference cell and the adjacent cell at the same energy storage charging monitoring point during the end of the full charge period is obtained, and the absolute value is taken to obtain the temperature difference value of a single pair of cells. The temperature difference between adjacent pairs of cells during the final stage of full charge is calculated to obtain the cell temperature difference gradient value. Extract the cell temperature difference gradient values ​​that are positive and greater than the cell temperature difference gradient threshold, sort them according to the time series, integrate the continuous cell temperature difference gradient values ​​in the time series, and calculate the summation and mean to obtain the unit temperature difference mutation analysis value. It should be noted that the cell temperature gradient threshold refers to the critical value used to determine whether the temperature difference between cells has increased abruptly. Its core function is to distinguish between normal small fluctuations in cell temperature difference and abnormal rapid increases caused by abrupt changes in cell heat generation patterns, thermal inertia lag, or intensified local overheating. The physical meaning is that when the calculated cell temperature gradient value is less than or equal to the cell temperature gradient threshold, it indicates that the temperature difference between the reference cell and its adjacent cells changes slowly within the normal range, which is a normal heat distribution fluctuation during charging and is not caused by a sudden change in local heat generation. When the calculated cell temperature gradient value is greater than the cell temperature gradient threshold and the sign is positive, it indicates that the temperature difference between the reference cell and the adjacent cell increases rapidly and continuously in a short period of time, reflecting that there is a significant local heat generation acceleration, heat accumulation, and heat transfer lag at the location of the reference cell, that is, a sudden temperature difference behavior has occurred. The mean and standard deviation of the unit temperature difference mutation analysis values ​​during the end of the full charge period were calculated separately to obtain the mean and standard deviation of the unit temperature difference mutation analysis. The suspected temperature difference mutation analysis values ​​were then obtained by using the coefficient of variation formula. It is understandable that the suspected temperature difference mutation analysis value means that the temperature difference mutation analysis value of each unit measures the degree of dispersion of the temperature difference mutation analysis value around the average value of the unit temperature difference mutation analysis. Specifically, if the suspected temperature difference mutation analysis value is larger, it means that the temperature difference mutation of the cell fluctuates more during the end of the full charge period. If the suspected temperature difference mutation analysis value is smaller, it means that the temperature difference mutation of the cell fluctuates less during the end of the full charge period. If the suspected temperature difference mutation analysis value is greater than the suspected temperature difference mutation analysis threshold, it indicates that the cell temperature difference mutation fluctuates greatly during the end of the full charge period, which is displayed as a heat generation mutation thermal inertia signal. If the suspected temperature difference mutation analysis value is less than or equal to the suspected temperature difference mutation analysis threshold, it indicates that the temperature difference mutation of the battery cell fluctuates less during the end of the full charge period, and is displayed as a non-mutation thermal inertia signal of heat generation.

[0025] Step 4: When a sudden change in thermal inertia signal occurs, analyze the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia. If a low frequency matching signal occurs, obtain the battery temperature sampling frequency adjustment amount, and manage and adjust the current battery temperature sampling frequency according to the battery temperature sampling frequency adjustment amount. In some embodiments, all current battery temperature monitoring points during the end of the full charge period are extracted, as well as the current temperature sampling value corresponding to each current battery temperature monitoring point. The current temperature sampling value corresponding to each current battery temperature monitoring point is compared with the cell temperature value of the reference cell at the energy storage charging monitoring point. The current battery temperature monitoring point corresponding to the current temperature sampling value that overlaps with the cell temperature value of the reference cell at the energy storage charging monitoring point is taken as the current sampling temperature point. The sampling interval ratio is calculated as the proportion of the time length between the energy storage charging monitoring point and the current sampling temperature point to the time length of the end period of full charging. The average of all sampling interval ratios is summed to obtain the sampling interval analysis value. If the sampling interval analysis value is less than or equal to the sampling interval analysis threshold, it indicates that the time interval between temperature sampling points is short and the current temperature sampling frequency matching degree is high, which is displayed as a high sampling frequency matching signal. If the sampling interval analysis value is greater than the sampling interval analysis threshold, it indicates that the time interval between temperature sampling points is long and the current temperature sampling frequency matching degree is low, which is displayed as a low sampling frequency matching signal. Compare the magnitudes of all sampling interval ratios, select the maximum sampling interval ratio and the minimum sampling interval ratio, calculate the average value, and then perform reciprocal processing to obtain the electric temperature sampling frequency adjustment amount. The current battery temperature sampling frequency is summed with the battery temperature sampling frequency adjustment amount to obtain the battery temperature sampling frequency management adjustment value; The specific solution of this invention is as follows: When the assessment result of the degree of latent local overheating is high local overheating, the change in battery cell temperature is identified by heat generation abrupt change, and it is determined whether battery thermal inertia occurs. When a heat generation abrupt change thermal inertia signal occurs, the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia is determined. If a low sampling frequency matching signal occurs, the battery temperature sampling frequency adjustment amount is obtained, and the current battery temperature sampling frequency is managed and adjusted according to the battery temperature sampling frequency adjustment amount. This can record rapid temperature changes in a timely manner, reduce temperature data errors caused by improper sampling frequency, and achieve the purpose of dynamic temperature control for energy storage battery thermal management.

[0026] Example 3 Please see Figure 3 As shown, this embodiment of the invention provides an energy storage battery thermal management system, including the following modules: Time Period Determination Module: During the charging process of the energy storage battery, the energy storage charging monitoring cycle is set, and the end period of full charge of the energy storage battery is identified and determined. Overheat assessment module: Monitors and analyzes the temperature of battery cells during the final stage of a full charge of the energy storage battery, and assesses the degree of hidden local overheating of the energy storage battery during the final stage of a full charge. Thermal mutation analysis module: When the assessment result of the degree of latent local overheating is high local overheating, the module identifies the thermal mutation of the battery cell temperature change and determines whether battery thermal inertia has occurred. Management and Adjustment Module: When a sudden change in thermal inertia signal occurs, determine the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia. If a low frequency matching signal occurs, obtain the battery temperature sampling frequency adjustment amount and perform management and adjustment operations on the current battery temperature sampling frequency according to the battery temperature sampling frequency adjustment amount.

[0027] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A thermal management method for an energy storage battery, characterized in that: include: During the charging process of the energy storage battery, an energy storage charging monitoring cycle is set to identify the end period of full charge and determine the end period of full charge. Temperature monitoring and analysis of battery cells during the final stage of full charge of energy storage batteries are conducted, and the degree of hidden local overheating of energy storage batteries during the final stage of full charge is evaluated. When the assessment result of the degree of latent local overheating is high local overheating, the heat generation mutation of the battery cell temperature change is identified, and it is determined whether battery thermal inertia occurs. When a sudden change in thermal inertia signal occurs, determine the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia. If a low frequency matching signal occurs, obtain the battery temperature sampling frequency adjustment amount, and manage and adjust the current battery temperature sampling frequency according to the battery temperature sampling frequency adjustment amount.

2. The energy storage battery thermal management method according to claim 1, characterized in that: The process of identifying energy storage batteries at the end of a full charge phase is as follows: Each energy storage charging monitoring cycle is equally divided into several energy storage charging monitoring points. The current total charging voltage of the energy storage battery at each energy storage charging monitoring point is obtained, and a total charging voltage change curve is constructed. A charging cutoff voltage line parallel to the X-axis is drawn on the total charging voltage change curve. The difference between the current total charging voltage and the charging cutoff voltage within each voltage analysis pair is taken as the absolute value to obtain the charging cutoff analysis value. Obtain the charging cutoff analysis value within the charging cutoff interval, extract the X-coordinate corresponding to the charging cutoff analysis value on the total charging voltage change curve, and compare the extracted X-coordinate before and after time. Select the X-coordinate at the beginning of time and the X-coordinate at the end of time to determine the suspected full charging period.

3. The thermal management method for an energy storage battery according to claim 1, characterized in that: The process for determining the final stage of a fully charged battery is as follows: The mean and standard deviation of the current total charging voltage during the suspected full charging period are calculated separately to obtain the mean and standard deviation of the current total charging voltage. Substitute the current total charging voltage standard deviation and the current total charging voltage mean into the coefficient of variation formula to output the suspected full charge voltage analysis value. If the suspected full charge voltage analysis value is less than or equal to the suspected full charge voltage analysis threshold, it is the end of the full charge period.

4. The thermal management method for an energy storage battery according to claim 1, characterized in that: The process of monitoring and analyzing the temperature of battery cells during the final stage of a full charge of an energy storage battery is as follows: Arbitrarily select a battery cell as the reference cell, and the battery cells adjacent to the reference cell as the neighboring cells. The area constructed by the reference cell and all the neighboring cells is used as the cell temperature analysis area. Obtain the cell temperature value of the reference cell at the energy storage charging monitoring point, and calculate the difference between the reference cell and the cell temperature value of each neighboring cell at the energy storage charging monitoring point. Take the absolute value and then sum the average values ​​to obtain the area temperature difference analysis value. If the regional temperature difference analysis value is greater than the regional temperature difference analysis threshold, the analyzed cell temperature analysis area will be marked as the cell abnormal temperature area, and the corresponding energy storage charging monitoring point will be marked as the cell abnormal temperature monitoring point.

5. The thermal management method for an energy storage battery according to claim 4, characterized in that: The assessment process for the degree of latent localized overheating is as follows: The number of continuous abnormal temperature monitoring points in the cell abnormal temperature zone during the end of the full charge period is counted, and the proportion of this number to the total number of energy storage charging monitoring points during the end of the full charge period is used to obtain the regional abnormal temperature duration value. After summing and averaging the regional temperature difference analysis values ​​corresponding to the continuous cell temperature monitoring points, the ratio of the average value to the cell calibration temperature is calculated to obtain the regional temperature difference degree value. The local overheating analysis value is obtained by summing the regional temperature variation duration value and the regional temperature variation degree value. If the local overheating analysis value is greater than the local overheating analysis threshold, it is displayed as a high local overheating signal.

6. The thermal management method for an energy storage battery according to claim 1, characterized in that: The process of identifying thermal abrupt changes in battery cell temperature is as follows: The reference cell in the cell temperature range is combined with each adjacent cell to obtain multiple cell temperature analysis pairs. The temperature difference between the reference cell and the adjacent cells at the same energy storage charging monitoring point during the end of the full charge period is obtained. The absolute value is taken to obtain the temperature difference value of a single pair of cells. The temperature difference between adjacent pairs of cells during the final stage of full charge is calculated to obtain the cell temperature difference gradient value. Extract the cell temperature gradient values ​​that are positive and greater than the cell temperature gradient threshold, sort them according to the time series, integrate the continuous cell temperature gradient values ​​in the time series, and calculate the summation and mean to obtain the unit temperature difference mutation analysis value.

7. The thermal management method for an energy storage battery according to claim 6, characterized in that: The process for determining the occurrence of battery thermal inertia is as follows: The mean and standard deviation of the unit temperature difference mutation analysis values ​​during the end of the full charge period were calculated separately to obtain the mean and standard deviation of the unit temperature difference mutation analysis. The suspected temperature difference mutation analysis values ​​were then obtained by using the coefficient of variation formula. If the suspected temperature difference mutation analysis value is greater than the suspected temperature difference mutation analysis threshold, it will be displayed as a heat generation mutation thermal inertia signal.

8. The thermal management method for an energy storage battery according to claim 1, characterized in that: The analysis process for the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia is as follows: Extract all current battery temperature monitoring points during the final stage of full charge, and the current temperature sampling value corresponding to each current battery temperature monitoring point. Compare the current temperature sampling value corresponding to each current battery temperature monitoring point with the cell temperature value of the reference cell at the energy storage charging monitoring point. The current battery temperature monitoring point corresponding to the current temperature sampling value that coincides with the cell temperature value of the reference cell at the energy storage charging monitoring point is taken as the current sampling temperature point. The time interval between the energy storage charging monitoring point and the current sampling temperature point is obtained, and the proportion of the time interval between them to the end of the full charge period is used as the sampling interval ratio.

9. The thermal management method for an energy storage battery according to claim 1, characterized in that: The process of acquiring the battery temperature sampling frequency adjustment value and managing and adjusting the current battery temperature sampling frequency is as follows: The average of all sampling interval ratios is calculated to obtain the sampling interval analysis value. If the sampling interval analysis value is greater than the sampling interval analysis threshold, it is displayed as a low-degree sampling frequency matching signal. The average of the maximum and minimum sampling interval ratios is calculated and then the reciprocal is processed to obtain the electric temperature sampling frequency adjustment amount. The current battery temperature sampling frequency is summed with the battery temperature sampling frequency adjustment amount to obtain the battery temperature sampling frequency management adjustment value.

10. A thermal management system for an energy storage battery, characterized in that: Includes the following modules: Time Period Determination Module: During the charging process of the energy storage battery, the energy storage charging monitoring cycle is set, and the end period of full charge of the energy storage battery is identified and determined. Overheat assessment module: Monitors and analyzes the temperature of battery cells during the final stage of a full charge of the energy storage battery, and assesses the degree of hidden local overheating of the energy storage battery during the final stage of a full charge. Thermal mutation analysis module: When the assessment result of the degree of latent local overheating is high local overheating, the module identifies the thermal mutation of the battery cell temperature change and determines whether battery thermal inertia has occurred. Management and Adjustment Module: When a sudden change in thermal inertia signal occurs, determine the matching degree between the current battery temperature sampling frequency and the current battery thermal inertia. If a low frequency matching signal occurs, obtain the battery temperature sampling frequency adjustment amount and perform management and adjustment operations on the current battery temperature sampling frequency according to the battery temperature sampling frequency adjustment amount.

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