Liquid cooling and air cooling composite thermal management system of large-scale lithium battery energy storage power station

Abstract

The invention discloses a liquid cooling and air cooling composite thermal management system of a large-scale lithium battery energy storage power station, relates to the field of energy storage, and solves the problem of poor thermal management effect of an existing liquid cooling and air cooling composite thermal management system. The heat dissipation prediction module is used for carrying out trend analysis on the unit heat production power curve and the air cooling rated heat dissipation power of a target energy storage unit, carrying out heat dissipation mode prediction on the target energy storage unit according to an analysis result to obtain heat dissipation mode prediction data, and carrying out heat dissipation prediction on the target energy storage unit according to the heat dissipation mode prediction data. And the composite heat management module is used for carrying out heat dissipation power distribution on the target energy storage unit matched with the mixed heat dissipation mode according to the unit heat production power curve and the heat dissipation mode prediction data, the heat dissipation mode switching efficiency of the energy storage unit can be improved, and therefore the liquid cooling and air cooling composite heat management capacity is improved.

Description

Technical Field

[0001] This invention belongs to the field of energy storage and relates to thermal management technology, specifically a liquid-cooled and air-cooled composite thermal management system for large-scale lithium battery energy storage power stations. Background Technology

[0002] Existing liquid-cooled and air-cooled hybrid thermal management systems have the following drawbacks when performing hybrid thermal management: 1. Existing liquid-cooled and air-cooled composite thermal management systems mainly adjust the heat dissipation power dynamically based on the real-time heat generation data of the energy storage unit. However, there is a data transmission delay between heat generation and heat dissipation power adjustment. Specifically, the system needs to first collect heat generation indicators such as battery temperature and current through sensors, then calculate and issue heat dissipation power commands through the control unit, and finally drive the liquid-cooled pump or air-cooled fan to perform adjustment. In this process, the time difference between data acquisition, transmission and processing will cause a time deviation between heat dissipation power adjustment and actual heat generation demand, forming adjustment lag, thereby reducing the real-time efficiency of the heat dissipation device. 2. Existing liquid-cooled and air-cooled combined thermal management systems mostly use preset heat generation thresholds. However, such fixed thresholds are difficult to adapt to the dynamic heat dissipation needs of special site scenarios. In high-temperature and high-humidity environments, the efficiency of air convection heat transfer decreases, and the actual heat dissipation capacity of the air-cooled mode may be lower than the expected threshold setting. This results in the system still relying on liquid cooling but not increasing power in time. In low-temperature and dry environments, the efficiency of air cooling increases, but the threshold is not dynamically adjusted, which may cause liquid cooling to be started too early, resulting in energy waste and ultimately affecting the overall heat dissipation performance and economy of the system.

[0003] To address this, we propose a liquid-cooled and air-cooled composite thermal management system for large-scale lithium battery energy storage power stations. Summary of the Invention

[0004] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a liquid-cooled and air-cooled composite thermal management system for large-scale lithium battery energy storage power stations. This invention aims to improve the heat dissipation management efficiency of the liquid-cooled and air-cooled composite thermal management system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a liquid-cooled and air-cooled composite thermal management system for large-scale lithium battery energy storage power stations, the specific working process of each module is as follows: Data acquisition module: Analyzes temperature changes in the target thermal management cycle to match a temperature matching management cycle. Filters out effective matching cycles from the temperature matching management cycle by analyzing changes in light intensity. Analyzes the charging heat generation power changes of the target energy storage unit within the temperature matching management cycle to plot the periodic discharge heat power curve. Analyzes the power generation heat generation power changes of the target energy storage unit within the effective matching cycle to plot the periodic charging heat power curve. Divides the target thermal management cycle into single-phase thermal management cycles and two-phase thermal management cycles. Combines the periodic discharge heat power curve and the periodic charging heat power curve to create unit heat generation power curves for different thermal management cycles, obtaining heat generation power acquisition data. Heat dissipation prediction module: Performs trend analysis on the heat generation power curve of the unit and the rated heat dissipation power of air cooling, and predicts the heat dissipation mode of the target energy storage unit based on the analysis results, and obtains heat dissipation mode prediction data. Composite thermal management module: Based on the unit's heat generation power curve and heat dissipation mode prediction data, it allocates heat dissipation power to the target energy storage unit that is adapted to the hybrid heat dissipation mode.

[0006] Furthermore, the heat generation power data is acquired, as follows: The energy storage units contained in a large-scale lithium battery energy storage power station are acquired to obtain multiple individual energy storage units. A target energy storage unit is randomly selected from the acquired individual energy storage units, and a target thermal management cycle is created for the target energy storage unit. Predict the discharge heat power of the target energy storage unit and plot the periodic discharge heat power curve based on the prediction results; Predict the charging heat power of the target energy storage unit and create a periodic charging heat power curve based on the prediction results; The solar radiation period is obtained during the solar radiation period on the natural date of the target thermal management cycle. If the target thermal management cycle is within the solar radiation period, it is classified as a two-phase thermal management cycle; otherwise, it is classified as a single-phase thermal management cycle. If the target thermal management cycle is a single-phase thermal management cycle, then the periodic discharge thermal power curve is set as the unit's heat generation power curve. If the target thermal management cycle is a two-phase thermal management cycle, then a comprehensive analysis is performed on the periodic discharge thermal power curve and the periodic power generation thermal power curve, and the unit's thermal power curve is created based on the analysis results.

[0007] Furthermore, the thermal power curve of the periodic discharge is obtained, as follows: The spatial area where the target energy storage unit is located is named the target unit area. The temperature change curve of the target unit area during the target thermal management cycle is plotted to obtain the target temperature change curve. Multiple historical thermal management cycles were selected from the historical operating periods corresponding to the target energy storage unit, and a sample thermal management cycle was selected. The temperature change of the target unit area within the target thermal management cycle was obtained and the curve was plotted to obtain the sample temperature change curve. The sample temperature change curve and the target temperature change curve are transferred to the temperature curve coordinate system. Temperature curve trend deviation analysis is performed in the temperature curve coordinate system. Based on the analysis results, the temperature matching management cycle is screened out from the historical thermal management cycle. The discharge heat generation power of the target energy storage unit in the temperature matching management cycle is analyzed. Based on the analysis results, the discharge heat generation power of the target energy storage unit in the target thermal management cycle is predicted, and the cycle discharge heat power curve is plotted based on the prediction results.

[0008] Furthermore, the discharge heat generation power of the target energy storage unit is analyzed, as follows: Create a periodic heat generation coordinate system, collect the discharge heat generation power of the target energy storage unit in each temperature matching management cycle in real time, and plot it as a discharge heat generation curve in the periodic heat generation coordinate system to obtain multiple matching discharge heat generation curves. In the periodic heat generation coordinate system, create a heat release comparison moving line, mark several feature points with equal longitudinal distances in the heat release comparison moving line, and set the straight line area intercepted by any two adjacent feature points as a straight line intercept sub-region to obtain multiple straight line intercept sub-regions. When the heat release ratio moving line is at the characteristic coordinate point, the number of intersection points between each line intercept sub-region and the matching discharge heat generation curve is obtained, the number of intersection points of multiple region curves is obtained, and the number of intersection points of multiple region curves is numerically analyzed. Based on the analysis results, the target heat release sub-region is obtained, and the median of the ordinate of the target heat release sub-region is obtained to obtain the predicted discharge heat generation power corresponding to the characteristic coordinate point. The target discharge heat management area is traversed by using a heat release ratio moving straight line to obtain the predicted discharge heat generation power corresponding to each horizontal coordinate point, and set as the vertical coordinate to obtain multiple predicted discharge coordinate points. The predicted discharge coordinate points are connected sequentially from left to right to obtain the periodic discharge heat power curve.

[0009] Furthermore, the thermal power curve of the cycle charging is obtained, as follows: The photovoltaic charging area corresponding to the target energy storage unit is named the target charging area. The change in light intensity in the target charging area during the target thermal management cycle is plotted to obtain the target light intensity change curve. Select any sample illumination monitoring cycle from the temperature matching management cycle, obtain the change in illumination intensity of the target charging area within the sample illumination monitoring cycle and plot the curve to obtain the sample illumination intensity change curve. The sample light intensity change curve and the target light intensity change curve are transferred to the same plane rectangular coordinate system to obtain the light curve coordinate system. Under the light curve coordinate system, the light trend deviation analysis is performed on the sample light intensity change curve and the target light intensity change curve. Based on the analysis results, multiple effective matching management cycles are screened out from the temperature matching management cycle. The discharge heat generation power of the target energy storage unit in the effective matching management cycle is analyzed. Based on the analysis results, the power generation heat generation power of the target thermal management cycle is predicted. Based on the prediction results, the power generation heat generation power curve of the cycle is plotted.

[0010] Furthermore, the heat production power curve of the unit was obtained, as follows: The periodic discharge thermal power curve and the periodic power generation thermal power curve are plotted together on the same Cartesian coordinate system. The horizontal axis is collected for the period covered by the target thermal management cycle to obtain multiple characteristic horizontal axes. The vertical axis corresponding to the characteristic horizontal axis in the periodic discharge thermal power curve is obtained to obtain the first vertical axis value. The vertical axis corresponding to the characteristic horizontal axis in the charging thermal power curve is obtained to obtain the second vertical axis value. The sum of the first vertical axis value and the second vertical axis value is calculated to obtain multiple characteristic vertical axes. The coordinate points formed by the characteristic horizontal axis and the corresponding vertical axis are marked as characteristic coordinate points. The characteristic coordinate points are connected sequentially from left to right to obtain the unit thermal power curve corresponding to the target thermal management cycle.

[0011] Furthermore, the heat dissipation pattern prediction data is acquired, as follows: The heat generation power curve of the unit is obtained based on the heat dissipation mode prediction data, and a heat generation efficiency coordinate system of the unit is created. The rated air-cooled heat dissipation power corresponding to the target energy storage unit is obtained. In the heat generation efficiency coordinate system of the unit, the coordinate point corresponding to the rated air-cooled heat dissipation power on the y-axis is obtained to obtain the rated air-cooled coordinate point. A straight line perpendicular to the y-axis is drawn through the rated air-cooled coordinate point to obtain the rated air-cooled characteristic line. Trend analysis is performed based on the rated characteristic straight line of air cooling and the heat generation power curve of the unit. Based on the analysis results, the heat dissipation mode of the target energy storage unit is predicted, and heat dissipation mode prediction data is obtained.

[0012] Furthermore, the heat dissipation mode of the target energy storage unit is predicted, as follows: If the unit's heat production power curve is completely below the air-cooled rated characteristic line, then it is predicted that the target energy storage unit is adapted to the air-cooled heat dissipation mode. If the unit's heat production power curve is not below the rated characteristic line of air cooling, the x-axis region of the unit's heat production power curve below the rated characteristic line of air cooling is obtained to obtain the characteristic heat dissipation region. The maximum vertical axis deviation between the rated characteristic line of air cooling and the unit's heat production power curve in the characteristic heat dissipation region is obtained to obtain the air cooling heat dissipation efficiency deviation. The preset range of air cooling heat dissipation efficiency deviation is obtained. If the air-cooled heat dissipation efficiency deviation is within the preset range, the target energy storage unit is predicted to be adapted to a hybrid heat dissipation mode; otherwise, the target energy storage unit is predicted to be adapted to a liquid-cooled heat dissipation mode.

[0013] Furthermore, the heat dissipation power is allocated to the target energy storage unit as follows: Acquire heat dissipation mode prediction data, and identify target energy storage units in mixed heat dissipation modes based on the heat dissipation mode prediction data; Obtain the unit's heat production efficiency coordinate system, and obtain the time points when the vertical axis of the unit's heat production power curve changes according to the unit's heat production efficiency coordinate system. This yields multiple heat production power change time points. Mark the time period formed by any two consecutive heat production power change time points as the heat dissipation power allocation time period, thus obtaining multiple heat dissipation power allocation time periods. Then, perform power allocation for each heat dissipation power allocation time period. Randomly select one sample power distribution period from the multiple heat dissipation power distribution periods. Based on the heat production power curve of the unit, obtain the average heat production power of the target energy storage unit during the sample power distribution period to get the average heat production power of the period. Obtain the average ambient temperature of the heat dissipation area of ​​the target energy storage unit during the sample power distribution period to get the average ambient temperature value. Calculate the ratio of the average ambient temperature value to the average heat production power of the period to get the thermal environment load ratio. Obtain the preset range of thermal load ratio. If the thermal load ratio is within the preset range, the sample power allocation period is divided into the air-cooled dominant period. If the thermal load ratio is not within the preset range, the sample power allocation period is divided into the liquid-cooled dominant period. Heat dissipation power is allocated for the air-cooled dominant period and the liquid-cooled dominant period respectively.

[0014] Furthermore, the heat dissipation power distribution process is as follows: If the sample power allocation period is divided into the air-cooled dominant period, multiple effective matching management cycles are obtained based on the heat dissipation mode prediction data. In the effective matching management cycle, the historical periods corresponding to the sample power allocation period are obtained to obtain multiple power sample periods. The air-cooled heat dissipation power of the target energy storage unit in each power sample period is obtained to obtain multiple historical sample air-cooled heat dissipation powers. The sum of the average and standard deviation of the historical sample air-cooled heat dissipation power is calculated to obtain the first regulated heat dissipation power. The difference between the average heat generation power of the period and the first regulated heat dissipation power is calculated to obtain the second regulated heat dissipation power. Then, the air-cooled heat dissipation device of the target energy storage unit is adjusted to the first regulated heat dissipation power, and the liquid-cooled heat dissipation device is adjusted to the second regulated heat dissipation power. If the sample power allocation period is divided into liquid cooling-dominated periods, the liquid cooling power of the target energy storage unit in each power sample period is obtained, and multiple historical sample liquid cooling power is obtained. The sum of the mean and standard deviation of the historical sample liquid cooling power is calculated to obtain the third regulating heat dissipation power. The difference between the average heat generation power of the period and the third regulating heat dissipation power is calculated to obtain the fourth regulating heat dissipation power. Then, the liquid cooling heat dissipation device of the target energy storage unit is adjusted to the third regulating heat dissipation power, and the air cooling heat dissipation device is adjusted to the fourth regulating heat dissipation power.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention analyzes temperature changes in the target thermal management cycle to match a temperature matching management cycle, analyzes light intensity changes in the target thermal management cycle to select an effective matching cycle from the temperature matching management cycle. It is difficult to analyze the changes in the charging heat generation power of the target energy storage unit within the temperature matching management cycle to predict the periodic discharge heat power curve. By analyzing the changes in the power generation heat generation power of the target energy storage unit within the effective matching cycle, the periodic charging heat power curve is predicted. Combining the periodic discharge heat power curve and the periodic charging heat power curve, heat generation power curves of the unit are created for single-phase thermal management cycles and two-phase thermal management cycles, respectively. The heat dissipation power is adjusted according to the heat dissipation power curve, which can improve the real-time performance of the heat dissipation power adjustment process, thereby improving the heat dissipation efficiency of the heat dissipation device. 2. This invention performs trend analysis on the heat generation power curve of the unit and the rated air-cooled heat dissipation power of the energy storage unit. Based on the analysis results, it predicts the heat dissipation mode of the energy storage unit and allocates heat dissipation power to the target energy storage unit with mixed heat dissipation mode accordingly. This can ensure the dynamic adaptability of the heat dissipation device of the energy storage unit and thus improve the heat dissipation mode switching efficiency of the energy storage unit. Attached Figure Description

[0016] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1This is an overall system block diagram of the present invention; Figure 2 This is the temperature curve coordinate system in this invention; Figure 3 This is the periodic heat generation coordinate system in this invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. Example

[0019] Please see Figure 1 The present invention provides a technical solution: a liquid-cooled and air-cooled composite thermal management system for a large-scale lithium battery energy storage power station, comprising a data acquisition module, a heat dissipation prediction module, a composite thermal management module and a server. The data acquisition module, the heat dissipation prediction module and the composite thermal management module are respectively connected to the server, and the server controls the data acquisition module, the heat dissipation prediction module and the composite thermal management module respectively. The data acquisition module analyzes temperature changes in the target thermal management cycle to match a temperature matching management cycle. It also analyzes light intensity changes in the target thermal management cycle to select effective matching cycles from the temperature matching management cycle. The module analyzes the changes in charging heat generation power of the target energy storage unit within the temperature matching management cycle to plot the periodic discharge heat power curve. It also analyzes the changes in power generation heat generation power of the target energy storage unit within the effective matching cycle to plot the periodic charging heat power curve. The target thermal management cycle is divided into single-phase thermal management cycle and two-phase thermal management cycle. Combining the periodic discharge heat power curve and the periodic charging heat power curve, the module creates heat generation power curves for the single-phase thermal management cycle and the two-phase thermal management cycle respectively, thus obtaining the heat generation power acquisition data. Specifically as follows: The energy storage units contained in a large-scale lithium battery energy storage power station are acquired, resulting in multiple individual energy storage units. One target energy storage unit is randomly selected from the acquired individual energy storage units. It should be noted here that: In this application, the large-scale lithium battery energy storage power station referred to herein is specifically a photovoltaic energy storage power station, and the individual energy storage unit referred to herein includes an independent charging and discharging device.

[0020] In the process of thermal management of large-scale lithium battery energy storage power stations, the time value corresponding to the current moment is taken as the start time of the cycle, the time point of the duration of the next mode after the current moment is taken as the end time of the cycle, and the time period between the start time of the cycle and the end time of the cycle is taken as the target thermal management cycle. It should be noted here that: In this application, the duration of the mode involved is specifically 20 minutes, and as the time value corresponding to the current moment changes, the start time and end time of the cycle also change accordingly, thereby realizing the dynamic update of the target thermal management cycle.

[0021] The discharge heat power of the target energy storage unit is predicted, and the periodic discharge heat power curve is obtained based on the prediction results. Specifically as follows: The spatial area where the target energy storage unit is located is named the target unit area. The temperature change curve of the target unit area during the target thermal management cycle is plotted by the temperature prediction device to obtain the target temperature change curve. Multiple historical thermal management cycles that are consistent with the target thermal management cycle period are selected from the historical working periods corresponding to the target energy storage unit. It should be noted here that: In this application, the historical thermal management cycle and the target thermal management cycle are the same within the corresponding natural date. If the target thermal management cycle is 20:00-20:20 of the current date, then the historical thermal management cycle is 20:00-20:20 of different historical dates. In this application, the discharge plan of the target energy storage unit in the historical thermal management cycle is consistent with the discharge plan of the target thermal management cycle. The discharge plan involved here includes, but is not limited to, discharge power, discharge time window and discharge target.

[0022] Randomly select one sample thermal management cycle from the multiple historical thermal management cycles obtained, obtain the temperature change of the target unit area within the target thermal management cycle and plot the curve to obtain the sample temperature change curve. Please see Figure 2The sample temperature change curve and the target temperature change curve are transferred to the same Cartesian coordinate system to obtain the temperature curve coordinate system. In the x-axis region covered by the target thermal management cycle in the temperature curve coordinate system, a straight line parallel to the y-axis is drawn to obtain the temperature movement comparison line. The ordinate of the intersection point of the temperature movement comparison line and the sample temperature change curve is obtained to obtain the first intersection point temperature value. The ordinate of the intersection point of the temperature movement comparison line and the target temperature change curve is obtained to obtain the second intersection point temperature value. The difference between the first intersection point temperature value and the second intersection point temperature value is calculated, and the absolute value of the obtained difference is taken to obtain the intersection point temperature deviation. The temperature movement comparison line is used to move and traverse the x-axis region covered by the target thermal management cycle. The temperature deviation of the intersection point corresponding to each x-axis coordinate point is obtained. The temperature deviation of the multiple intersection points is compared numerically, and the intersection point with the largest value is set as the peak value of the temperature trend deviation corresponding to the sample thermal management cycle. Repeat the process of obtaining the temperature trend deviation peak value corresponding to the sample thermal management cycle, obtain the temperature trend deviation peak value corresponding to each historical thermal management cycle, set a temperature trend deviation benchmark value, if the temperature trend deviation peak value is less than or equal to the temperature trend deviation benchmark value, then the historical thermal management cycle is screened into a temperature matching management cycle, if the temperature trend deviation peak value is greater than the temperature trend deviation benchmark value, then the historical thermal management cycle is screened into a temperature non-matching management cycle. It should be noted here that: In this application, the temperature trend deviation benchmark value is specifically set to 0.5 degrees Celsius.

[0023] The discharge heat generation power of the target energy storage unit in the temperature matching management cycle is analyzed. Based on the analysis results, the discharge heat generation power of the target energy storage unit in the target thermal management cycle is predicted. Based on the prediction results, the cycle discharge heat power curve is plotted. It should be noted here that: The above process, by constructing a temperature curve coordinate system and introducing a temperature movement comparison line, achieves a quantitative comparison of the temperature trends of historical thermal management cycles and target cycles. Its core advantage lies in the fact that, through dynamic calculation and peak filtering of the intersection temperature deviation, it can accurately identify historical cycles that highly match the temperature change trend of the target cycle, providing reliable data support for subsequent thermal management strategy optimization. Specifically, this method captures the degree of vertical deviation (intersection temperature deviation) between the sample curve and the target curve at each position by traversing the x-axis region covered by the target cycle, and extracts the maximum deviation value (peak temperature trend deviation) as an evaluation index. This can effectively distinguish between scenarios in historical cycles where the temperature change trend is similar (small deviation) or different (large deviation) from the target cycle. Combined with a preset deviation benchmark value, it filters out temperature matching management cycles, which can avoid the error of subjective experience judgment and provide more targeted historical data references for subsequent heat dissipation mode prediction, power allocation, and other aspects, thereby improving the adaptability and control accuracy of the composite thermal management system under complex operating conditions.

[0024] Specifically as follows: Please see Figure 3 In the existing Cartesian coordinate system, the time value is set as the horizontal axis and the heat production power is marked as the vertical axis to obtain the periodic heat production coordinate system; The discharge heat generation power of the target energy storage unit in each temperature matching management cycle is collected in real time and plotted as a discharge heat generation curve in the cycle heat generation coordinate system to obtain multiple matching discharge heat generation curves. It should be noted here that: In this application, the heat power generated by the battery during the release of electrical energy due to internal resistance and chemical reactions is described.

[0025] In the periodic heat generation coordinate system, the x-axis region covered by the target heat management cycle is set as the target discharge heat management region. A characteristic coordinate point is arbitrarily selected within the target discharge heat management region, and a straight line perpendicular to the x-axis is drawn through the characteristic coordinate point to obtain the heat release comparison movement line. Mark several feature points with equal longitudinal distances in the heat dissipation comparison moving line, and set the straight line area intercepted by any two adjacent feature points as the straight line intercept sub-region to obtain multiple straight line intercept sub-regions. It should be noted here that: In this application, any straight line interception sub-region includes the straight line region covered by the lower endpoint.

[0026] When the heat release comparison moving line is at the feature coordinate point, the number of intersection points between each straight line intercept sub-region and the matching discharge heat generation curve is obtained, resulting in the number of intersection points of multiple region curves. The obtained number of intersection points of multiple region curves is numerically compared. If there is no case where the maximum number of region curve intersection points is tied, the straight line intercept sub-region corresponding to the maximum number of region curve intersection points is marked as the target heat release sub-region. If there is a case where the maximum number of region curve intersection points is tied, the variance of the ordinate of the region curve intersection points of the straight line intercept sub-region corresponding to the maximum number of region curve intersection points is calculated. The straight line intercept sub-region corresponding to the smaller variance value is marked as the target heat release sub-region. The median of the ordinate of the target heat release sub-region is obtained to obtain the predicted discharge heat generation power corresponding to the feature coordinate point. The target discharge heat management area is traversed by using the heat release ratio moving straight line to obtain the predicted discharge heat generation power corresponding to each horizontal coordinate point, and set as the vertical coordinate to obtain multiple predicted discharge coordinate points. The multiple predicted discharge coordinate points are then connected from left to right to obtain the periodic discharge heat power curve. It should be noted here that: This process, by constructing a periodic heat generation coordinate system and combining dynamic analysis of the moving straight line of the heat release ratio, achieves accurate prediction and curve fitting of the discharge heat generation power of the target energy storage unit. Its core advantage lies in the following: by quantitatively analyzing the spatiotemporal correlation between the discharge heat generation curves of historical matching cycles and the target area, it can dynamically capture the local variation characteristics of heat generation power, generating a periodic discharge heat power curve that is more in line with actual operating conditions. This provides high-precision data support for the heat dissipation mode switching and power allocation of the composite thermal management system. Specifically, this method moves a vertical straight line within the target area and divides it into sub-regions. It counts the number of intersections between each sub-region and the historical curve (reflecting the concentration of heat generation power), and combines variance analysis to select the most representative target heat release sub-regions. Then, it extracts the predicted power of the feature points through the median of the vertical axis, which avoids the random error of a single data point and retains the local fluctuation characteristics of heat generation power. The final generated periodic discharge heat power curve can accurately reflect the heat generation trend of the unit at different times, providing a reliable basis for the coordinated control of liquid cooling / air cooling modes and the dynamic optimization of power allocation, thereby improving the thermal management efficiency and stability of the system under complex operating conditions.

[0027] Then, the charging heat power of the target energy storage unit is predicted, and the cycle charging heat power curve is obtained based on the prediction results. Specifically as follows: The photovoltaic charging area corresponding to the target energy storage unit is named the target charging area. The solar radiation monitoring device is used to plot the change in light intensity of the target charging area during the target thermal management cycle to obtain the target light intensity change curve. It should be noted here that: In this application, the discharge plan of the target energy storage unit in the historical thermal management cycle is consistent with the charging plan of the target thermal management cycle. The charging plan involved here includes, but is not limited to, the energy target, the charging time window and the real-time charging power.

[0028] Select any sample illumination monitoring cycle from the temperature matching management cycle, obtain the change in illumination intensity of the target charging area within the sample illumination monitoring cycle and plot the curve to obtain the sample illumination intensity change curve. The sample light intensity change curve and the target light intensity change curve are transferred to the same Cartesian coordinate system to obtain the light curve coordinate system. In the x-axis region covered by the target thermal management cycle in the light curve coordinate system, a straight line parallel to the y-axis is drawn to obtain the light movement comparison line. The ordinate of the intersection point of the light movement comparison line and the sample light intensity change curve is numerically obtained to obtain the first intersection point light value. The ordinate of the intersection point of the light movement comparison line and the target light intensity change curve is numerically obtained to obtain the second intersection point light value. The difference between the first intersection point light value and the second intersection point light value is calculated, and the absolute value of the obtained difference is taken to obtain the intersection point light deviation. The light movement comparison line is used to move and traverse the x-axis region covered by the target thermal management cycle, and the light deviation of the intersection point corresponding to each x-axis coordinate point is obtained. The light deviations of multiple intersection points are compared numerically, and the intersection point with the largest value is set as the peak value of the light trend deviation corresponding to the sample light monitoring cycle. Repeat the process of obtaining the peak value of the light trend deviation corresponding to the sample light monitoring cycle, obtain the peak value of the light trend deviation corresponding to each historical thermal management cycle, set a light trend deviation benchmark value, if the peak value of the light trend deviation is less than or equal to the benchmark value of the light trend deviation, then the temperature matching management cycle is screened as an effective matching management cycle, if the peak value of the light trend deviation is greater than the benchmark value of the light trend deviation, then the temperature matching management cycle is screened as an invalid matching management cycle. It should be noted here that: In this application, the light intensity referred to herein is specifically the visible light power received per unit area, and the reference value for the light trend deviation referred to herein is specifically set to 25 lux.

[0029] Analyze the discharge heat generation power of the target energy storage unit in the effective matching management cycle, predict the power generation heat generation power of the target thermal management cycle based on the analysis results, and draw the power generation heat generation power curve of the cycle based on the prediction results. It should be noted here that: In this application, the process for creating the periodic power generation heat production curve is consistent with the process for creating the periodic power discharge heat production curve.

[0030] The solar radiation period is obtained during the solar radiation period on the natural date of the target thermal management cycle. If the target thermal management cycle is within the solar radiation period, it is classified as a two-phase thermal management cycle. If the target thermal management cycle is not within the solar radiation period, it is classified as a single-phase thermal management cycle. It should be noted here that: In this application, if the time point at which a single energy storage unit begins to receive solar radiation is 7:34 and the time point at which the single energy storage unit ends to receive solar radiation is 18:04 in the natural date of the target thermal management cycle, then the period between 7:34 and 18:04 is the solar radiation period.

[0031] If the target thermal management cycle is a single-phase thermal management cycle, then the periodic discharge thermal power curve is set to the unit heat generation power curve corresponding to the target thermal management cycle. If the target thermal management cycle is a two-phase thermal management cycle, then the periodic discharge thermal power curve and the periodic power generation thermal power curve are comprehensively analyzed, and the unit thermal power curve corresponding to the target thermal management cycle is obtained based on the analysis results. Specifically as follows: The periodic discharge heat power curve and the periodic power generation heat power curve are plotted together on the same Cartesian coordinate system. The horizontal axis is collected for the period covered by the target thermal management cycle to obtain multiple characteristic horizontal axes. The vertical axis corresponding to the characteristic horizontal axis in the periodic discharge heat power curve is numerically obtained to obtain the first vertical axis value. The vertical axis corresponding to the characteristic horizontal axis in the charging heat power curve is numerically obtained to obtain the second vertical axis value. The sum of the first vertical axis value and the second vertical axis value is calculated to obtain multiple characteristic vertical axes. The coordinate points formed by the characteristic horizontal axis and the corresponding vertical axis are marked as characteristic coordinate points. The multiple characteristic coordinate points are connected from left to right to obtain the unit heat power curve corresponding to the target thermal management cycle. The unit's heat production power curve and multiple effective matching management cycles are defined as heat production power acquisition data; The heat dissipation prediction module performs trend analysis on the heat generation power curve of the unit and the air-cooled rated heat dissipation power of the target energy storage unit. Based on the analysis results, it predicts the heat dissipation mode of the target energy storage unit and obtains heat dissipation mode prediction data. Specifically as follows: The heat generation power curve of the unit is obtained based on the heat dissipation mode prediction data, and a heat generation efficiency coordinate system of the unit is created. The rated air-cooled heat dissipation power corresponding to the target energy storage unit is obtained. In the heat generation efficiency coordinate system of the unit, the coordinate point corresponding to the rated air-cooled heat dissipation power on the y-axis is obtained to obtain the rated air-cooled coordinate point. A straight line perpendicular to the y-axis is drawn through the rated air-cooled coordinate point to obtain the rated air-cooled characteristic line. It should be noted here that: In this application, the rated heat dissipation power of air cooling is the maximum amount of heat that the air cooling system can continuously and stably dissipate under standard operating conditions.

[0032] Trend analysis is performed based on the air-cooled rated characteristic straight line and the unit's heat generation power curve. Based on the analysis results, the heat dissipation mode of the target energy storage unit is predicted, and heat dissipation mode prediction data is obtained. Specifically as follows: If the unit's heat production power curve is completely below the air-cooled rated characteristic line, then it is predicted that the target energy storage unit is adapted to the air-cooled heat dissipation mode. If the unit's heat production power curve is not below the rated characteristic line of air cooling, the x-axis region of the unit's heat production power curve below the rated characteristic line of air cooling is obtained to obtain the characteristic heat dissipation region. The maximum vertical axis deviation between the rated characteristic line of air cooling and the unit's heat production power curve in the characteristic heat dissipation region is obtained to obtain the air cooling heat dissipation efficiency deviation. The preset range of air cooling heat dissipation efficiency deviation is obtained. If the deviation of air-cooled heat dissipation efficiency is within the preset range of air-cooled heat dissipation efficiency deviation, it is predicted that the target energy storage unit will be adapted to a hybrid heat dissipation mode. If the deviation of air cooling efficiency is not within the preset range of air cooling efficiency deviation, it is predicted that the target energy storage unit will be adapted to liquid cooling mode. It should be noted here that: In this application, the historical periods during which the target energy storage unit fully utilizes liquid cooling are obtained, and the minimum air cooling efficiency deviation of the target energy storage unit during the historical liquid cooling period is set as the upper limit of the preset range of air cooling efficiency deviation. The historical periods during which the target energy storage unit fully utilizes air cooling are obtained, and the minimum air cooling efficiency deviation of the target energy storage unit during the historical air cooling period is set as the lower limit of the preset range of air cooling efficiency deviation. In this application, the hybrid heat dissipation mode involved includes the case where the air cooling efficiency deviation is within the boundary of a preset range of air cooling efficiency deviation.

[0033] The composite thermal management module allocates heat dissipation power to the target energy storage unit that is adapted to the hybrid heat dissipation mode based on the unit's heat generation power curve and heat dissipation mode prediction data. Specifically as follows: Acquire heat dissipation mode prediction data, and identify target energy storage units in mixed heat dissipation modes based on the heat dissipation mode prediction data; Obtain the unit's heat production efficiency coordinate system, and obtain the time points when the vertical axis of the unit's heat production power curve changes according to the unit's heat production efficiency coordinate system. This yields multiple heat production power change time points. Mark the time period formed by any two consecutive heat production power change time points as the heat dissipation power allocation time period, thus obtaining multiple heat dissipation power allocation time periods. Randomly select one sample power allocation period from the multiple heat dissipation power allocation periods, and allocate heat dissipation power to the target energy storage unit in the sample power allocation period. Specifically as follows: The average heat generation power of the target energy storage unit during the sample power allocation period is obtained based on the unit's heat generation power curve. The average heat generation power during the period is obtained. The average ambient temperature of the heat dissipation area of ​​the target energy storage unit during the sample power allocation period is obtained. The average ambient temperature value is obtained. The ratio of the average ambient temperature value to the average heat generation power during the period is calculated to obtain the thermal environment load ratio. Obtain the preset range of thermal load ratio. If the thermal load ratio is within the preset range, the sample power allocation period is divided into the air-cooled dominant period. If the thermal load ratio is not within the preset range, the sample power allocation period is divided into the liquid-cooled dominant period. It should be noted here that: In this application, the lower limit of the preset range of thermal load ratio is 0, that is, the target energy storage unit does not generate heat. The historical air-cooled dominant period is obtained, and the thermal load ratio corresponding to each historical air-cooled dominant period is obtained. The values ​​of the multiple thermal load ratios are compared, and the thermal load ratio with the largest value is set as the upper limit of the preset range of thermal load ratio. In this application, the period during which air cooling dominates includes the case where the thermal load ratio is at the boundary of a preset range of thermal load ratios; In this application, since the thermal conductivity of the coolant is much higher than that of air, it can quickly transfer heat from the heat source to the external environment, overcoming the inhibition of air cooling heat dissipation by the high temperature environment. Liquid cooling achieves temperature uniformity control through distributed pipelines, avoiding local hot spots that are easy to generate in air cooling at high temperatures. Liquid cooling has a large specific heat capacity, which can absorb instantaneous heat, and the pump control flow regulation response is rapid, adapting to rapid fluctuations in heat generation power. In contrast, air cooling has a slower response and may not have sufficient airflow at high temperatures. Therefore, the higher the thermal load ratio, the better the liquid cooling device can meet the demand for efficient and stable heat dissipation in high temperature and high load scenarios.

[0034] If the sample power allocation period is divided into the air-cooled dominant period, the heat dissipation power is allocated to the target energy storage unit. Specifically as follows: Based on the heat dissipation pattern prediction data, multiple effective matching management cycles are obtained. In the effective matching management cycle, the historical time period corresponding to the sample power allocation time period is obtained to obtain multiple power sample time periods. The air-cooled heat dissipation power of the target energy storage unit in each power sample time period is obtained to obtain multiple historical sample air-cooled heat dissipation powers. The sum of the average and standard deviation of the historical sample air-cooled heat dissipation power is calculated to obtain the first regulated heat dissipation power. The difference between the average heat generation power of the time period and the first regulated heat dissipation power is calculated to obtain the second regulated heat dissipation power. Then, the air-cooled heat dissipation device of the target energy storage unit is adjusted to the first regulated heat dissipation power, and the liquid-cooled heat dissipation device is adjusted to the second regulated heat dissipation power. If the sample power allocation period is divided into a liquid cooling-dominated period, the heat dissipation power is allocated to the target energy storage unit. Specifically as follows: Obtain the liquid cooling power of the target energy storage unit for each power sample period, obtain the liquid cooling power of multiple historical samples, calculate the sum of the mean and standard deviation of the liquid cooling power of the historical samples to obtain the third regulating cooling power, calculate the difference between the average heat generation power of the period and the third regulating cooling power to obtain the fourth regulating cooling power, then adjust the liquid cooling device of the target energy storage unit to the third regulating cooling power and the air cooling device to the fourth regulating cooling power.

[0035] It should be noted here that: In this application, the heat generation power referred to herein is specifically the heat power generated by the energy storage unit during operation due to electrochemical conversion, resistance loss, mechanical friction, etc., and the heat dissipation power referred to herein is specifically the heat power actually removed from the energy storage unit by the heat dissipation system (liquid cooling + air cooling).

[0036] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A liquid-air hybrid thermal management system for large-scale lithium battery energy storage power stations, characterized in that, Comprise: Data acquisition module: temperature change analysis is carried out on target thermal management cycle to match temperature matching management cycle, effective matching period is screened out from temperature matching management cycle through illumination intensity change analysis, charging heat generation power change of target energy storage unit in temperature matching management cycle is analyzed to draw period discharge heat power curve, power generation heat generation power change of target energy storage unit in effective matching period is analyzed to draw period charging heat power curve, target thermal management cycle is divided into single-phase thermal management cycle and double-phase thermal management cycle, unit heat generation power curve is created for different thermal management cycles in combination with period discharge heat power curve and period charging heat power curve, and heat generation power acquisition data is obtained; Heat dissipation prediction module: trend analysis is carried out on unit heat generation power curve and air cooling rated heat dissipation power, heat dissipation mode prediction is carried out on target energy storage unit according to analysis result, and heat dissipation mode prediction data is obtained; Composite thermal management module: heat dissipation power distribution is carried out on target energy storage unit adapting to mixed heat dissipation mode according to unit heat generation power curve and heat dissipation mode prediction data.

2. The liquid-air hybrid thermal management system of large-scale lithium battery energy storage power station according to claim 1, characterized in that, The heat generation power acquisition data is obtained, and the specific process is as follows: A plurality of single energy storage units are obtained by acquiring the energy storage units contained in a large-scale lithium battery energy storage power station, a target energy storage unit is selected from the single energy storage units, and a target thermal management cycle is created for the target energy storage unit; The discharge heat generation power of the target energy storage unit is predicted, and the period discharge heat power curve is drawn according to the prediction result; The charging heat generation power of the target energy storage unit is predicted, and the period charging heat power curve is created according to the prediction result; The solar radiation period is obtained by acquiring the solar radiation period of the natural date of the target thermal management cycle, if the target thermal management cycle is in the solar radiation period, the target thermal management cycle is divided into a double-phase thermal management cycle, if not, the target thermal management cycle is divided into a single-phase thermal management cycle; If the target thermal management cycle is a single-phase thermal management cycle, the period discharge heat power curve is set as the unit heat generation power curve; If the target thermal management cycle is a double-phase thermal management cycle, the period discharge heat power curve and the period generation heat power curve are comprehensively analyzed, and the unit heat generation power curve is created according to the analysis result.

3. The liquid-air hybrid thermal management system of large-scale lithium battery energy storage power station according to claim 2, characterized in that, The period discharge heat power curve is obtained, and the specific process is as follows: The space area where the target energy storage unit is located is named as the target unit area, the temperature change of the target unit area in the target thermal management cycle is drawn, and the target temperature change curve is obtained; A plurality of historical thermal management cycles are screened out from the historical working period corresponding to the target energy storage unit, and a sample thermal management cycle is selected, the temperature change of the target unit area in the target thermal management cycle is obtained and drawn, and the sample temperature change curve is obtained; The sample temperature change curve and the target temperature change curve are transferred to the temperature curve coordinate system, the temperature curve trend deviation is analyzed in the temperature curve coordinate system, and the temperature matching management cycle is screened out from the historical thermal management cycle according to the analysis result; The discharge heat generation power of the target energy storage unit in the target thermal management period is analyzed, and the discharge heat generation power of the target energy storage unit in the target thermal management period is predicted according to the analysis result. The period discharge heat power curve is drawn according to the prediction result.

4. The liquid-air hybrid thermal management system of large-scale lithium battery energy storage power station according to claim 3, characterized in that, The discharge heat generation power of the target energy storage unit is analyzed, and the discharge heat generation power of the target energy storage unit in each temperature matching management period is collected in real time, and is drawn as a discharge heat generation curve in the period heat generation coordinate system, and a plurality of matching discharge heat generation curves are obtained. In the period heat generation coordinate system, a heat dissipation comparison moving straight line is created, a plurality of characteristic points with equal longitudinal distance are marked in the heat dissipation comparison moving straight line, and a straight line region intercepted by any two adjacent characteristic points is set as a straight line intercepting sub-region, and a plurality of straight line intercepting sub-regions are obtained. When the heat dissipation comparison moving straight line is at the characteristic coordinate point, the number of intersection points of each straight line intercepting sub-region and the matching discharge heat generation curve is obtained, a plurality of region curve intersection point numbers are obtained, and numerical analysis is performed on the obtained plurality of region curve intersection point numbers. According to the analysis result, the target heat dissipation sub-region is obtained, the median of the longitudinal coordinate of the target heat dissipation sub-region is obtained, and the predicted discharge heat generation power corresponding to the characteristic coordinate point is obtained. The target discharge heat management region is traversed using the heat dissipation comparison moving straight line, the predicted discharge heat generation power corresponding to each horizontal coordinate point is obtained, and is set as the vertical coordinate, a plurality of predicted discharge coordinate points are obtained, and the predicted discharge coordinate points are connected from left to right in turn, and the period discharge heat power curve is obtained. The period charging heat power curve is obtained, and the specific process is as follows:

5. The liquid-air hybrid thermal management system of large-scale lithium battery energy storage power station according to claim 4, characterized in that, The target charging region corresponding to the target energy storage unit is named as the target charging region, and the change of the light intensity of the target charging region in the target thermal management period is drawn as a curve, and the target light intensity change curve is obtained. A sample light monitoring period is selected from the temperature matching management period, the light intensity change of the target charging region in the sample light monitoring period is obtained and drawn as a curve, and the sample light intensity change curve is obtained. In the same plane rectangular coordinate system, the sample light intensity change curve and the target light intensity change curve are obtained, and the light curve coordinate system is obtained. In the light curve coordinate system, the light trend deviation of the sample light intensity change curve and the target light intensity change curve is analyzed, and a plurality of effective matching management periods are screened out from the temperature matching management period according to the analysis result. The discharge heat generation power of the target energy storage unit in the target thermal management period is analyzed, and the discharge heat generation power of the target energy storage unit in the target thermal management period is predicted according to the analysis result. The period discharge heat power curve is drawn according to the prediction result. The unit heat generation power curve is obtained, and the specific process is as follows:

6. The liquid-air hybrid thermal management system of large-scale lithium battery energy storage power station of claim 2, wherein, ​ The periodic discharging heat power curve and the periodic generating heat power curve are drawn in the same plane rectangular coordinate system, the horizontal coordinates of the time period covered by the target heat management period are collected to obtain a plurality of characteristic horizontal coordinates, the vertical coordinates corresponding to the characteristic horizontal coordinates in the periodic discharging heat power curve are obtained to obtain first vertical coordinate values, the vertical coordinates corresponding to the characteristic horizontal coordinates in the charging heat power curve are obtained to obtain second vertical coordinate values, the sum of the first vertical coordinate values and the second vertical coordinate values is calculated to obtain a plurality of characteristic vertical coordinates, the coordinate points composed of the characteristic horizontal coordinates and the corresponding vertical coordinates are marked as characteristic coordinate points, and the characteristic coordinate points are sequentially connected from left to right to obtain the unit heat power curve corresponding to the target heat management period.

7. The liquid-air hybrid thermal management system of large-scale lithium battery energy storage power station of claim 1, wherein, The heat dissipation mode prediction data is obtained, and the specific process is as follows: The unit heat power curve is obtained according to the heat dissipation mode prediction data, and a unit heat efficiency coordinate system is created, the wind cooling rated heat dissipation power corresponding to the target energy storage unit is obtained, the coordinate point corresponding to the wind cooling rated heat dissipation power on the coordinate y-axis in the unit heat efficiency coordinate system is obtained to obtain a rated wind cooling coordinate point, and a straight line perpendicular to the coordinate y-axis is drawn through the rated wind cooling coordinate point to obtain a wind cooling rated characteristic straight line; The trend analysis is performed according to the wind cooling rated characteristic straight line and the unit heat power curve, and the target energy storage unit is predicted according to the analysis result to obtain the heat dissipation mode prediction data.

8. The liquid-air hybrid thermal management system of large-scale lithium battery energy storage power station according to claim 7, characterized in that, The heat dissipation mode prediction data is obtained, and the specific process is as follows: If the unit heat power curve is completely below the wind cooling rated characteristic straight line, it is predicted that the target energy storage unit adapts to the wind cooling heat dissipation mode; If the unit heat power curve is not completely below the wind cooling rated characteristic straight line, the coordinate x-axis region of the unit heat power curve below the wind cooling rated characteristic straight line is obtained to obtain a characteristic heat dissipation region, the maximum vertical coordinate deviation of the wind cooling rated characteristic straight line and the unit heat power curve in the characteristic heat dissipation region is obtained to obtain a wind cooling heat dissipation efficiency deviation, and a wind cooling heat dissipation efficiency deviation preset interval is obtained; If the wind cooling heat dissipation efficiency deviation is in the wind cooling heat dissipation efficiency deviation preset interval, it is predicted that the target energy storage unit adapts to the mixed heat dissipation mode, and if not, it is predicted that the target energy storage unit adapts to the liquid cooling heat dissipation mode.

9. The liquid-air hybrid thermal management system of large-scale lithium battery energy storage power station of claim 1, wherein, The heat dissipation power distribution of the target energy storage unit is performed, and the specific process is as follows: The heat dissipation mode prediction data is obtained, and the target energy storage unit in the mixed heat dissipation mode is obtained according to the heat dissipation mode prediction data; The unit heat efficiency coordinate system is obtained, the time points at which the vertical coordinates of the unit heat power curve change are obtained according to the unit heat efficiency coordinate system to obtain a plurality of heat power change time points, the time period composed of any two consecutive heat power change time points is marked as a heat dissipation power distribution time period to obtain a plurality of heat dissipation power distribution time periods, and power distribution is performed on each heat dissipation power distribution time period; In any selected sample power allocation period of the obtained multiple heat dissipation power allocation periods, the average heat production power of the target energy storage unit in the sample power allocation period is obtained according to the unit heat production power curve, to obtain the period average heat production power, the average ambient temperature of the target energy storage unit heat dissipation area in the sample power allocation period is obtained, to obtain the average ambient temperature value, the ratio of the average ambient temperature value and the period average heat production power is calculated, to obtain the thermal environment load ratio; Obtain the thermal environment load ratio preset interval, if the thermal environment load ratio is in the thermal environment load ratio preset interval, the sample power allocation period is divided into the air cooling dominated period, if the thermal environment load ratio is not in the thermal environment load ratio preset interval, the sample power allocation period is divided into the liquid cooling dominated period, the heat dissipation power allocation is carried out respectively for the air cooling dominated period and the liquid cooling dominated period.

10. The liquid-air hybrid thermal management system of large-scale lithium battery energy storage power station according to claim 9, characterized in that, The heat dissipation power allocation process is as follows: If the sample power allocation period is divided into the air cooling dominated period, a plurality of effective matching management periods are obtained according to the heat dissipation mode prediction data, in the effective matching management period, the historical period corresponding to the sample power allocation period is obtained, to obtain a plurality of power sample periods, the air cooling heat dissipation power corresponding to each power sample period of the target energy storage unit is obtained, to obtain a plurality of historical sample air cooling heat dissipation powers, the sum of the average and the standard deviation of the historical sample air cooling heat dissipation powers is calculated, to obtain the first adjusted heat dissipation power, the difference between the period average heat production power and the first adjusted heat dissipation power is calculated, to obtain the second adjusted heat dissipation power, then the air cooling heat dissipation device of the target energy storage unit is adjusted to the first adjusted heat dissipation power, and the liquid cooling heat dissipation device is adjusted to the second adjusted heat dissipation power; If the sample power allocation period is divided into the liquid cooling dominated period, the liquid cooling heat dissipation power corresponding to each power sample period of the target energy storage unit is obtained, to obtain a plurality of historical sample liquid cooling heat dissipation powers, the sum of the average and the standard deviation of the historical sample liquid cooling heat dissipation powers is calculated, to obtain the third adjusted heat dissipation power, the difference between the period average heat production power and the third adjusted heat dissipation power is calculated, to obtain the fourth adjusted heat dissipation power, then the liquid cooling heat dissipation device of the target energy storage unit is adjusted to the third adjusted heat dissipation power, and the air cooling heat dissipation device is adjusted to the fourth adjusted heat dissipation power.

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