A method and system for analyzing liquid-cooled energy storage applications in multiple scenarios

Abstract

This invention discloses a method and system for analyzing liquid-cooled energy storage applications in multiple scenarios. First, a three-dimensional visualized energy storage model is constructed based on the energy storage area layout information, and layout analysis and area division are performed. Next, a multi-scenario energy storage task plan is set, and temperature control data for each energy storage analysis area is collected and analyzed. The correlation of temperature control change characteristics is determined using the Pearson coefficient, and temperature-affected areas are screened. Then, real-time temperature control data is obtained based on the real-time energy storage plan to identify the core high-temperature areas. The ant colony algorithm is used to simulate heat paths in the energy storage model to generate heat prediction information. Finally, based on the prediction information, heat distribution prediction and comprehensive temperature control analysis are performed on the energy storage area. A regional temperature control scheme is formulated through a liquid-cooled temperature control platform to achieve effective temperature control management of the energy storage area.

Description

Technical Field

[0001] This invention relates to the field of energy storage heat dissipation analysis, and more specifically, to a method and system for analyzing liquid-cooled energy storage applications in multiple scenarios. Background Technology

[0002] With the increasing demand for energy storage, battery energy storage is also constantly developing. Liquid-cooled energy storage technology has gained widespread attention due to its efficient heat dissipation, energy saving, and environmental friendliness. Liquid-cooled energy storage systems remove the heat generated by batteries or energy storage components through liquid circulation, effectively improving the performance and reliability of the energy storage system.

[0003] However, traditional energy storage temperature control analysis often relies on manual monitoring of temperature data for regulation, which is time-consuming, labor-intensive, and inefficient. Furthermore, it lacks prediction of heat distribution within the energy storage system and efficient liquid cooling control methods, resulting in low liquid cooling efficiency and insufficient intelligence in energy storage system monitoring. Therefore, there is an urgent need for a method and system capable of comprehensively, in real-time, and accurately analyzing and controlling the temperature of energy storage devices. Summary of the Invention

[0004] This invention overcomes the shortcomings of existing technologies and proposes a method and system for analyzing liquid-cooled energy storage applications in multiple scenarios.

[0005] The first aspect of this invention provides a method for analyzing liquid-cooled energy storage applications in multiple scenarios, including:

[0006] S1: Obtain energy storage area layout information and construct an energy storage model based on 3D visualization based on the energy storage area layout information;

[0007] S2: Through the energy storage model, the layout of energy storage equipment is analyzed, and the overall energy storage area is divided into regions to obtain multiple energy storage analysis areas;

[0008] S3: Set up energy storage task plans for multiple scenarios. Within the task plan, collect temperature control data from multiple energy storage analysis areas through the temperature control monitoring platform, perform linear change analysis on the temperature control data, and obtain temperature control change characteristics.

[0009] S4: Analyze whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and adjacent energy storage analysis areas. The correlation judgment is based on the Pearson coefficient analysis, and temperature control affected areas are screened out and marked from the energy storage analysis areas.

[0010] S5: Based on the real-time energy storage plan, acquire real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out the core high-temperature areas;

[0011] S6: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the movement range to construct the ant colony model. Each energy storage analysis area is used as a movement path point. Based on the first pheromone, the pheromone of each movement path point is initialized. If the movement path point corresponds to the temperature control influence area, the corresponding movement path point is initialized based on the second pheromone. The core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path.

[0012] S7: Based on heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis for multiple energy storage analysis areas, and generate regional temperature control schemes through a liquid-cooled temperature control platform.

[0013] In this solution, S1 specifically refers to:

[0014] Obtain information on the layout of energy storage areas;

[0015] The energy storage area layout information includes the area of ​​the energy storage area, the area outline, the area equipment layout information, and the number of energy storage devices.

[0016] A 3D visualization-based energy storage model is constructed based on the energy storage area layout information.

[0017] In this solution, S2 specifically refers to:

[0018] The energy storage model is used to analyze the layout of energy storage devices, the distribution of energy storage units, and to divide the energy storage model into multiple energy storage analysis areas, ensuring that each energy storage analysis area includes a preset number of energy storage units.

[0019] In this solution, S3 specifically refers to:

[0020] Set up energy storage task plans for multiple scenarios, and collect temperature control data from multiple energy storage analysis areas through a temperature control monitoring platform during the task plan period;

[0021] Temperature control data includes temperature values ​​for the corresponding time period, liquid cooling equipment operation information, and temperature distribution information;

[0022] The energy storage task plan includes the energy storage device discharge and charging task process;

[0023] Based on the temperature control data, the corresponding temperature values ​​are sorted according to the time dimension, and the numerical change characteristics are calculated and analyzed based on linear change to generate temperature control change characteristics.

[0024] In this solution, S4 specifically refers to:

[0025] The energy storage analysis area is used as the unit of analysis.

[0026] The temperature control variation characteristics of an energy storage analysis area are marked as the first characteristic data;

[0027] The temperature control change characteristics corresponding to multiple adjacent energy storage analysis areas in the one energy storage analysis area are marked as second feature data to obtain multiple second feature data;

[0028] A linear correlation analysis based on the Pearson coefficient is performed between the first feature data and each second feature data to obtain multiple Pearson coefficients;

[0029] Determine whether each Pearson coefficient is within the preset coefficient range. If so, it means that there is a high degree of influence between the energy storage analysis area and the corresponding adjacent energy storage analysis area. Count the number K of adjacent energy storage analysis areas with a high degree of influence.

[0030] If K is greater than the predetermined quantity, then the energy storage analysis area will be marked as the temperature control influence area.

[0031] In this solution, S5 specifically refers to:

[0032] Based on the current real-time energy storage plan, real-time temperature control data of multiple energy storage analysis areas are collected through the temperature control monitoring platform;

[0033] Temperature change analysis is performed based on real-time temperature control data to calculate the temperature growth rate and maximum temperature value;

[0034] Determine whether the temperature growth rate and maximum temperature value of each energy storage analysis area are both greater than the preset range. If so, mark the energy storage analysis area as the core high temperature area.

[0035] In this solution, S6 specifically refers to:

[0036] Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the movement range to construct the ant colony model, which is used to simulate the ant colony's running path.

[0037] Each energy storage analysis area is used as a movement path point, and each movement path point has multiple movement options in different directions;

[0038] Define a first pheromone and a second pheromone, where the value of the first pheromone is less than that of the second pheromone.

[0039] Based on the first pheromone, initialize the pheromone of each movement path point;

[0040] If the movement path point corresponds to the temperature control affected area, then the corresponding movement path point is initialized based on the second pheromone;

[0041] A preset number of ant colonies is set, with the core high-temperature area as the starting point of the ant colonies. A preset number of ant colonies are set for each core high-temperature area. The ant colonies make path decisions through pheromones and heuristic information, and the path simulation is repeated until the preset number of simulations is reached. The termination condition for each path simulation is that the ant colony's movement path reaches the preset maximum length.

[0042] Heat path simulation is performed based on the ant colony model. The corresponding movement paths are recorded, and high-frequency paths are selected from the movement paths as heat prediction simulation paths.

[0043] Based on the heat prediction simulation path, regional mapping of heat trends is performed in the energy storage model to generate regional heat prediction information.

[0044] In this solution, S7 specifically refers to:

[0045] Based on heat forecast information, analyze the distribution and trend of heat in each energy storage analysis area;

[0046] Heat distribution prediction and comprehensive temperature control analysis are performed through a liquid-cooled temperature control platform, and a priority table and temperature control parameters for liquid-cooled temperature control are set for each energy storage analysis area.

[0047] The application is based on the liquid cooling temperature control priority table and temperature control parameters to generate regional temperature control schemes.

[0048] A second aspect of the present invention also provides a liquid-cooled energy storage application analysis system based on multiple scenarios. The system includes a memory and a processor. The memory includes a liquid-cooled energy storage application analysis program based on multiple scenarios. When executed by the processor, the liquid-cooled energy storage application analysis program based on multiple scenarios performs the following steps:

[0049] S1: Obtain energy storage area layout information and construct an energy storage model based on 3D visualization based on the energy storage area layout information;

[0050] S2: Through the energy storage model, the layout of energy storage equipment is analyzed, and the overall energy storage area is divided into regions to obtain multiple energy storage analysis areas;

[0051] S3: Set up energy storage task plans for multiple scenarios. Within the task plan, collect temperature control data from multiple energy storage analysis areas through the temperature control monitoring platform, perform linear change analysis on the temperature control data, and obtain temperature control change characteristics.

[0052] S4: Analyze whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and adjacent energy storage analysis areas. The correlation judgment is based on the Pearson coefficient analysis, and temperature control affected areas are screened out and marked from the energy storage analysis areas.

[0053] S5: Based on the real-time energy storage plan, acquire real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out the core high-temperature areas;

[0054] S6: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the movement range to construct the ant colony model. Each energy storage analysis area is used as a movement path point. Based on the first pheromone, the pheromone of each movement path point is initialized. If the movement path point corresponds to the temperature control influence area, the corresponding movement path point is initialized based on the second pheromone. The core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path.

[0055] S7: Based on heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis for multiple energy storage analysis areas, and generate regional temperature control schemes through a liquid-cooled temperature control platform.

[0056] A third aspect of the present invention also provides a computer-readable storage medium comprising a liquid-cooled energy storage application analysis program based on multiple scenarios. When the liquid-cooled energy storage application analysis program based on multiple scenarios is executed by a processor, it implements the steps of the liquid-cooled energy storage application analysis method based on multiple scenarios as described in any of the preceding claims.

[0057] This invention discloses a method and system for analyzing liquid-cooled energy storage applications in multiple scenarios. First, a three-dimensional visualized energy storage model is constructed based on the energy storage area layout information, and layout analysis and area division are performed. Next, a multi-scenario energy storage task plan is set, and temperature control data for each energy storage analysis area is collected and analyzed. The correlation of temperature control change characteristics is determined using the Pearson coefficient, and temperature-affected areas are screened. Then, real-time temperature control data is obtained based on the real-time energy storage plan to identify the core high-temperature areas. The ant colony algorithm is used to simulate heat paths in the energy storage model to generate heat prediction information. Finally, based on the prediction information, heat distribution prediction and comprehensive temperature control analysis are performed on the energy storage area. A regional temperature control scheme is formulated through a liquid-cooled temperature control platform to achieve effective temperature control management of the energy storage area. Attached Figure Description

[0058] Figure 1 A flowchart of an analysis method for liquid-cooled energy storage applications under multiple scenarios according to the present invention is shown;

[0059] Figure 2 A block diagram of a liquid-cooled energy storage application analysis system based on multiple scenarios according to the present invention is shown. Detailed Implementation

[0060] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0061] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0062] Figure 1 The flowchart of the present invention provides an analysis method for liquid-cooled energy storage applications in multiple scenarios.

[0063] like Figure 1 As shown, the first aspect of the present invention provides a method for analyzing liquid-cooled energy storage applications in multiple scenarios, including:

[0064] S1: Obtain energy storage area layout information and construct an energy storage model based on 3D visualization based on the energy storage area layout information;

[0065] S2: Through the energy storage model, the layout of energy storage equipment is analyzed, and the overall energy storage area is divided into regions to obtain multiple energy storage analysis areas;

[0066] S3: Set up energy storage task plans for multiple scenarios. Within the task plan, collect temperature control data from multiple energy storage analysis areas through the temperature control monitoring platform, perform linear change analysis on the temperature control data, and obtain temperature control change characteristics.

[0067] S4: Analyze whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and adjacent energy storage analysis areas. The correlation judgment is based on the Pearson coefficient analysis, and temperature control affected areas are screened out and marked from the energy storage analysis areas.

[0068] S5: Based on the real-time energy storage plan, acquire real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out the core high-temperature areas;

[0069] S6: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the movement range to construct the ant colony model. Each energy storage analysis area is used as a movement path point. Based on the first pheromone, the pheromone of each movement path point is initialized. If the movement path point corresponds to the temperature control influence area, the corresponding movement path point is initialized based on the second pheromone. The core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path.

[0070] S7: Based on heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis for multiple energy storage analysis areas, and generate regional temperature control schemes through a liquid-cooled temperature control platform.

[0071] According to an embodiment of the present invention, S1 specifically includes:

[0072] Obtain information on the layout of energy storage areas;

[0073] The energy storage area layout information includes the area of ​​the energy storage area, the area outline, the area equipment layout information, and the number of energy storage devices.

[0074] A 3D visualization-based energy storage model is constructed based on the energy storage area layout information.

[0075] It should be noted that the energy storage area can be a containerized battery energy storage area, which includes various energy storage-related equipment, such as battery system devices, temperature control and monitoring devices, liquid cooling devices, etc. The energy storage model enables visualized monitoring and regional temperature control analysis of the energy storage area.

[0076] According to an embodiment of the present invention, step S2 specifically includes:

[0077] The energy storage model is used to analyze the layout of energy storage devices, the distribution of energy storage units, and to divide the energy storage model into multiple energy storage analysis areas, ensuring that each energy storage analysis area includes a preset number of energy storage units.

[0078] It should be noted that the energy storage unit is preset information, such as a preset battery energy storage unit. One unit includes a preset number of batteries. By dividing the area through energy storage units, precise temperature control analysis and liquid cooling regulation can be achieved in the future.

[0079] According to an embodiment of the present invention, step S3 specifically includes:

[0080] Set up energy storage task plans for multiple scenarios, and collect temperature control data from multiple energy storage analysis areas through a temperature control monitoring platform during the task plan period;

[0081] Temperature control data includes temperature values ​​for the corresponding time period, liquid cooling equipment operation information, and temperature distribution information;

[0082] The energy storage task plan includes the energy storage device discharge and charging task process;

[0083] Based on the temperature control data, the corresponding temperature values ​​are sorted according to the time dimension, and the numerical change characteristics are calculated and analyzed based on linear change to generate temperature control change characteristics.

[0084] It should be noted that the energy storage task plan can be selected from a list of plans with multiple scenarios based on historical tasks. The plan includes various energy storage tasks, and temperature changes under multiple scenarios are analyzed by collecting corresponding temperature control data. Each energy storage analysis area corresponds to a temperature control change characteristic.

[0085] According to an embodiment of the present invention, step S4 specifically includes:

[0086] The energy storage analysis area is used as the unit of analysis.

[0087] The temperature control variation characteristics of an energy storage analysis area are marked as the first characteristic data;

[0088] The temperature control change characteristics corresponding to multiple adjacent energy storage analysis areas in the one energy storage analysis area are marked as second feature data to obtain multiple second feature data;

[0089] A linear correlation analysis based on the Pearson coefficient is performed between the first feature data and each second feature data to obtain multiple Pearson coefficients;

[0090] Determine whether each Pearson coefficient is within the preset coefficient range. If so, it means that there is a high degree of influence between the energy storage analysis area and the corresponding adjacent energy storage analysis area. Count the number K of adjacent energy storage analysis areas with a high degree of influence.

[0091] If K is greater than the predetermined quantity, then the energy storage analysis area will be marked as the temperature control influence area.

[0092] It should be noted that the predetermined quantity is generally 2. For an energy storage analysis region, the number of Pearson coefficients calculated is equal to the number of adjacent energy storage analysis regions. An energy storage analysis region may have multiple adjacent energy storage analysis regions.

[0093] This invention analyzes the changing characteristics of historical temperature control data, analyzes the temperature control change relationships between adjacent areas based on each energy storage analysis area, determines the existence of an influence relationship through linear correlation, and filters out locations with high influence on neighboring areas, marking the temperature control influence area. This area has certain temperature control change characteristics and influence characteristics in the energy storage system, and is more likely to affect the heat trend. In the subsequent ant colony algorithm simulation, corresponding dynamic parameter correction is required to simulate the real heat trend.

[0094] According to an embodiment of the present invention, step S5 specifically includes:

[0095] Based on the current real-time energy storage plan, real-time temperature control data of multiple energy storage analysis areas are collected through the temperature control monitoring platform;

[0096] Temperature change analysis is performed based on real-time temperature control data to calculate the temperature growth rate and maximum temperature value;

[0097] Determine whether the temperature growth rate and maximum temperature value of each energy storage analysis area are both greater than the preset range. If so, mark the energy storage analysis area as the core high temperature area.

[0098] It should be noted that the preset range includes the preset maximum temperature and the maximum temperature increase rate.

[0099] According to an embodiment of the present invention, step S6 specifically includes:

[0100] Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the movement range to construct the ant colony model, which is used to simulate the ant colony's running path.

[0101] Each energy storage analysis area is used as a movement path point, and each movement path point has multiple movement options in different directions;

[0102] Define a first pheromone and a second pheromone, where the value of the first pheromone is less than that of the second pheromone.

[0103] Based on the first pheromone, initialize the pheromone of each movement path point;

[0104] If the movement path point corresponds to the temperature control affected area, then the corresponding movement path point is initialized based on the second pheromone;

[0105] A preset number of ant colonies is set, with the core high-temperature area as the starting point of the ant colonies. A preset number of ant colonies are set for each core high-temperature area. The ant colonies make path decisions through pheromones and heuristic information, and the path simulation is repeated until the preset number of simulations is reached. The termination condition for each path simulation is that the ant colony's movement path reaches the preset maximum length.

[0106] Heat path simulation is performed based on the ant colony model. The corresponding movement paths are recorded, and high-frequency paths are selected from the movement paths as heat prediction simulation paths.

[0107] Based on the heat prediction simulation path, regional mapping of heat trends is performed in the energy storage model to generate regional heat prediction information.

[0108] It should be noted that the ant colony model and the energy storage model have the same region, path, and model size. After simulating the path using the ant colony model, the corresponding path information can be generated from the energy storage model through path mapping. Each energy storage analysis area corresponds one-to-one with each movement path point. In the improved ant colony model of this invention, no endpoint is set. The ant colony selects multiple paths based on the starting point. For the initial state pheromone, a lower pheromone is set for non-temperature-controlled areas and a higher pheromone is set for temperature-controlled areas. This makes the improved ant colony model more consistent with the actual heat trend and achieves a more accurate heat distribution model and heat path trend prediction. Regional heat prediction information includes the heat trend, temperature changes, and heat distribution information in each energy storage analysis area.

[0109] According to an embodiment of the present invention, step S7 specifically includes:

[0110] Based on heat forecast information, analyze the distribution and trend of heat in each energy storage analysis area;

[0111] Heat distribution prediction and comprehensive temperature control analysis are performed through a liquid-cooled temperature control platform, and a priority table and temperature control parameters for liquid-cooled temperature control are set for each energy storage analysis area.

[0112] The application is based on the liquid cooling temperature control priority table and temperature control parameters to generate regional temperature control schemes.

[0113] It should be noted that the liquid cooling temperature control priority table includes regional priority temperature control information.

[0114] It is worth noting that in traditional energy storage temperature control analysis, regulation is often based on manual monitoring of temperature control data, which is time-consuming, labor-intensive, and inefficient. Furthermore, there is a lack of prediction of the heat distribution of the energy storage system and efficient liquid cooling regulation methods, resulting in low efficiency of liquid cooling regulation and insufficient intelligence in energy storage system monitoring.

[0115] Based on this, this invention analyzes the temperature control characteristics of the corresponding energy storage area, predicts heat trends using an ant colony algorithm, and improves the relevant parameters of the algorithm model based on existing temperature control characteristics. It can define different pheromones based on different regional characteristics to simulate more realistic heat trends. Through heat prediction, corresponding temperature control schemes are set on the liquid-cooled temperature control platform, effectively enabling refined prediction and regulation of the energy storage area from both a holistic and regional perspective. This achieves comprehensive and efficient temperature control management of energy storage equipment, improves temperature control intelligence, and realizes efficient operation and optimized control of liquid-cooled equipment. Simultaneously, it enables visualized predictive analysis of temperature control simulation.

[0116] Liquid cooling equipment generally consists of circulating pumps, compressors, liquid cooling pipes, and liquid cooling units.

[0117] According to an embodiment of the present invention, it further includes:

[0118] During the first energy storage mission cycle, relevant heat prediction information and actual temperature control data were acquired;

[0119] Based on heat prediction information and actual temperature control data, the accuracy of temperature control prediction is evaluated for each energy storage analysis area, and multiple prediction accuracy rates are obtained.

[0120] The overall prediction accuracy is obtained by averaging the multiple prediction accuracies.

[0121] If the overall prediction accuracy is higher than the set accuracy, then in the second energy storage task cycle, the ant colony model parameters will be dynamically adjusted, and the ant colony parameters of the first energy storage task cycle will be used, while the preset number of simulations will be reduced by a percentage.

[0122] If the overall prediction accuracy is lower than the set accuracy, then the temperature control influence area and the core high temperature area are reset, and the first pheromone, second pheromone, and ant colony starting point parameters of the ant colony model are dynamically adjusted.

[0123] It should be noted that this invention compares the characteristics of completed energy storage task cycles with actual temperature control data. Through prediction accuracy analysis, it dynamically adjusts the ant colony model for multiple future cycles. If the accuracy meets expectations, the model data can be simplified, improving data analysis efficiency and optimizing the ant colony model process. This significantly reduces the amount of data analyzed by the model while maintaining heat prediction accuracy, thus reducing the data processing pressure on the server. If the accuracy does not meet expectations, the corresponding model parameters are reset based on the most recent temperature control data, dynamically adjusting the ant colony model so that the prediction process can dynamically adapt to the actual operation of the energy storage system. Simultaneously, through the above process, efficient simulation in multiple scenarios can be effectively achieved, improving server operating efficiency and the system operating efficiency of temperature control monitoring and liquid cooling platforms.

[0124] The reduction of the preset number of simulations by a percentage can be set from 10% to 50% to dynamically adjust the efficiency of the ant colony algorithm while ensuring prediction accuracy.

[0125] Figure 2 A block diagram of a liquid-cooled energy storage application analysis system based on multiple scenarios according to the present invention is shown.

[0126] A second aspect of the present invention also provides a liquid-cooled energy storage application analysis system 2 based on multiple scenarios. The system includes a memory 21 and a processor 22. The memory 21 includes a liquid-cooled energy storage application analysis program based on multiple scenarios. When the processor 22 executes the liquid-cooled energy storage application analysis program based on multiple scenarios, it performs the following steps:

[0127] S1: Obtain energy storage area layout information and construct an energy storage model based on 3D visualization based on the energy storage area layout information;

[0128] S2: Through the energy storage model, the layout of energy storage equipment is analyzed, and the overall energy storage area is divided into regions to obtain multiple energy storage analysis areas;

[0129] S3: Set up energy storage task plans for multiple scenarios. Within the task plan, collect temperature control data from multiple energy storage analysis areas through the temperature control monitoring platform, perform linear change analysis on the temperature control data, and obtain temperature control change characteristics.

[0130] S4: Analyze whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and adjacent energy storage analysis areas. The correlation judgment is based on the Pearson coefficient analysis, and temperature control affected areas are screened out and marked from the energy storage analysis areas.

[0131] S5: Based on the real-time energy storage plan, acquire real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out the core high-temperature areas;

[0132] S6: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the movement range to construct the ant colony model. Each energy storage analysis area is used as a movement path point. Based on the first pheromone, the pheromone of each movement path point is initialized. If the movement path point corresponds to the temperature control influence area, the corresponding movement path point is initialized based on the second pheromone. The core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path.

[0133] S7: Based on heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis for multiple energy storage analysis areas, and generate regional temperature control schemes through a liquid-cooled temperature control platform.

[0134] According to an embodiment of the present invention, S1 specifically includes:

[0135] Obtain information on the layout of energy storage areas;

[0136] The energy storage area layout information includes the area of ​​the energy storage area, the area outline, the area equipment layout information, and the number of energy storage devices.

[0137] A 3D visualization-based energy storage model is constructed based on the energy storage area layout information.

[0138] It should be noted that the energy storage area can be a containerized battery energy storage area, which includes various energy storage-related equipment, such as battery system devices, temperature control and monitoring devices, liquid cooling devices, etc. The energy storage model enables visualized monitoring and regional temperature control analysis of the energy storage area.

[0139] According to an embodiment of the present invention, step S2 specifically includes:

[0140] The energy storage model is used to analyze the layout of energy storage devices, the distribution of energy storage units, and to divide the energy storage model into multiple energy storage analysis areas, ensuring that each energy storage analysis area includes a preset number of energy storage units.

[0141] It should be noted that the energy storage unit is preset information, such as a preset battery energy storage unit. One unit includes a preset number of batteries. By dividing the area through energy storage units, precise temperature control analysis and liquid cooling regulation can be achieved in the future.

[0142] According to an embodiment of the present invention, step S3 specifically includes:

[0143] Set up energy storage task plans for multiple scenarios, and collect temperature control data from multiple energy storage analysis areas through a temperature control monitoring platform during the task plan period;

[0144] Temperature control data includes temperature values ​​for the corresponding time period, liquid cooling equipment operation information, and temperature distribution information;

[0145] The energy storage task plan includes the energy storage device discharge and charging task process;

[0146] Based on the temperature control data, the corresponding temperature values ​​are sorted according to the time dimension, and the numerical change characteristics are calculated and analyzed based on linear change to generate temperature control change characteristics.

[0147] It should be noted that the energy storage task plan can be selected from a list of plans with multiple scenarios based on historical tasks. The plan includes various energy storage tasks, and temperature changes under multiple scenarios are analyzed by collecting corresponding temperature control data. Each energy storage analysis area corresponds to a temperature control change characteristic.

[0148] According to an embodiment of the present invention, step S4 specifically includes:

[0149] The energy storage analysis area is used as the unit of analysis.

[0150] The temperature control variation characteristics of an energy storage analysis area are marked as the first characteristic data;

[0151] The temperature control change characteristics corresponding to multiple adjacent energy storage analysis areas in the one energy storage analysis area are marked as second feature data to obtain multiple second feature data;

[0152] A linear correlation analysis based on the Pearson coefficient is performed between the first feature data and each second feature data to obtain multiple Pearson coefficients;

[0153] Determine whether each Pearson coefficient is within the preset coefficient range. If so, it means that there is a high degree of influence between the energy storage analysis area and the corresponding adjacent energy storage analysis area. Count the number K of adjacent energy storage analysis areas with a high degree of influence.

[0154] If K is greater than the predetermined quantity, then the energy storage analysis area will be marked as the temperature control influence area.

[0155] It should be noted that the predetermined quantity is generally 2. For an energy storage analysis region, the number of Pearson coefficients calculated is equal to the number of adjacent energy storage analysis regions. An energy storage analysis region may have multiple adjacent energy storage analysis regions.

[0156] This invention analyzes the changing characteristics of historical temperature control data, analyzes the temperature control change relationships between adjacent areas based on each energy storage analysis area, determines the existence of an influence relationship through linear correlation, and filters out locations with high influence on neighboring areas, marking the temperature control influence area. This area has certain temperature control change characteristics and influence characteristics in the energy storage system, and is more likely to affect the heat trend. In the subsequent ant colony algorithm simulation, corresponding dynamic parameter correction is required to simulate the real heat trend.

[0157] According to an embodiment of the present invention, step S5 specifically includes:

[0158] Based on the current real-time energy storage plan, real-time temperature control data of multiple energy storage analysis areas are collected through the temperature control monitoring platform;

[0159] Temperature change analysis is performed based on real-time temperature control data to calculate the temperature growth rate and maximum temperature value;

[0160] Determine whether the temperature growth rate and maximum temperature value of each energy storage analysis area are both greater than the preset range. If so, mark the energy storage analysis area as the core high temperature area.

[0161] It should be noted that the preset range includes the preset maximum temperature and the maximum temperature increase rate.

[0162] According to an embodiment of the present invention, step S6 specifically includes:

[0163] Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the movement range to construct the ant colony model, which is used to simulate the ant colony's running path.

[0164] Each energy storage analysis area is used as a movement path point, and each movement path point has multiple movement options in different directions;

[0165] Define a first pheromone and a second pheromone, where the value of the first pheromone is less than that of the second pheromone.

[0166] Based on the first pheromone, initialize the pheromone of each movement path point;

[0167] If the movement path point corresponds to the temperature control affected area, then the corresponding movement path point is initialized based on the second pheromone;

[0168] A preset number of ant colonies is set, with the core high-temperature area as the starting point of the ant colonies. A preset number of ant colonies are set for each core high-temperature area. The ant colonies make path decisions through pheromones and heuristic information, and the path simulation is repeated until the preset number of simulations is reached. The termination condition for each path simulation is that the ant colony's movement path reaches the preset maximum length.

[0169] Heat path simulation is performed based on the ant colony model. The corresponding movement paths are recorded, and high-frequency paths are selected from the movement paths as heat prediction simulation paths.

[0170] Based on the heat prediction simulation path, regional mapping of heat trends is performed in the energy storage model to generate regional heat prediction information.

[0171] It should be noted that the ant colony model and the energy storage model have the same region, path, and model size. After simulating the path using the ant colony model, the corresponding path information can be generated from the energy storage model through path mapping. Each energy storage analysis area corresponds one-to-one with each movement path point. In the improved ant colony model of this invention, no endpoint is set. The ant colony selects multiple paths based on the starting point. For the initial state pheromone, a lower pheromone is set for non-temperature-controlled areas and a higher pheromone is set for temperature-controlled areas. This makes the improved ant colony model more consistent with the actual heat trend and achieves a more accurate heat distribution model and heat path trend prediction. Regional heat prediction information includes the heat trend, temperature changes, and heat distribution information in each energy storage analysis area.

[0172] According to an embodiment of the present invention, step S7 specifically includes:

[0173] Based on heat forecast information, analyze the distribution and trend of heat in each energy storage analysis area;

[0174] Heat distribution prediction and comprehensive temperature control analysis are performed through a liquid-cooled temperature control platform, and a priority table and temperature control parameters for liquid-cooled temperature control are set for each energy storage analysis area.

[0175] The application is based on the liquid cooling temperature control priority table and temperature control parameters to generate regional temperature control schemes.

[0176] It should be noted that the liquid cooling temperature control priority table includes regional priority temperature control information.

[0177] It is worth noting that in traditional energy storage temperature control analysis, regulation is often based on manual monitoring of temperature control data, which is time-consuming, labor-intensive, and inefficient. Furthermore, there is a lack of prediction of the heat distribution of the energy storage system and efficient liquid cooling regulation methods, resulting in low efficiency of liquid cooling regulation and insufficient intelligence in energy storage system monitoring.

[0178] Based on this, this invention analyzes the temperature control characteristics of the corresponding energy storage area, predicts heat trends using an ant colony algorithm, and improves the relevant parameters of the algorithm model based on existing temperature control characteristics. It can define different pheromones based on different regional characteristics to simulate more realistic heat trends. Through heat prediction, corresponding temperature control schemes are set on the liquid-cooled temperature control platform, effectively enabling refined prediction and regulation of the energy storage area from both a holistic and regional perspective. This achieves comprehensive and efficient temperature control management of energy storage equipment, improves temperature control intelligence, and realizes efficient operation and optimized control of liquid-cooled equipment. Simultaneously, it enables visualized predictive analysis of temperature control simulation.

[0179] Liquid cooling equipment generally consists of circulating pumps, compressors, liquid cooling pipes, and liquid cooling units.

[0180] A third aspect of the present invention also provides a computer-readable storage medium comprising a liquid-cooled energy storage application analysis program based on multiple scenarios. When the liquid-cooled energy storage application analysis program based on multiple scenarios is executed by a processor, it implements the steps of the liquid-cooled energy storage application analysis method based on multiple scenarios as described in any of the preceding claims.

[0181] This invention discloses a method and system for analyzing liquid-cooled energy storage applications in multiple scenarios. First, a three-dimensional visualized energy storage model is constructed based on the energy storage area layout information, and layout analysis and area division are performed. Next, a multi-scenario energy storage task plan is set, and temperature control data for each energy storage analysis area is collected and analyzed. The correlation of temperature control change characteristics is determined using the Pearson coefficient, and temperature-affected areas are screened. Then, real-time temperature control data is obtained based on the real-time energy storage plan to identify the core high-temperature areas. The ant colony algorithm is used to simulate heat paths in the energy storage model to generate heat prediction information. Finally, based on the prediction information, heat distribution prediction and comprehensive temperature control analysis are performed on the energy storage area. A regional temperature control scheme is formulated through a liquid-cooled temperature control platform to achieve effective temperature control management of the energy storage area.

[0182] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0183] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0184] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0185] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0187] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing liquid-cooled energy storage applications in multiple scenarios, characterized in that, include: S1: Obtain energy storage area layout information and construct an energy storage model based on 3D visualization based on the energy storage area layout information; S2: Through the energy storage model, the layout of energy storage equipment is analyzed, and the overall energy storage area is divided into regions to obtain multiple energy storage analysis areas; S3: Set up energy storage task plans for multiple scenarios. Within the task plan, collect temperature control data from multiple energy storage analysis areas through the temperature control monitoring platform, perform linear change analysis on the temperature control data, and obtain temperature control change characteristics. S4: Analyze whether there is a correlation between the temperature control change characteristics of each energy storage analysis area and adjacent energy storage analysis areas. The correlation judgment is based on the Pearson coefficient analysis, and temperature control affected areas are screened out and marked from the energy storage analysis areas. S5: Based on the real-time energy storage plan, acquire real-time temperature control data of multiple energy storage analysis areas, perform temperature change analysis based on the real-time temperature control data, and screen out the core high-temperature areas; S6: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the movement range to construct the ant colony model. Each energy storage analysis area is used as a movement path point. Based on the first pheromone, the pheromone of each movement path point is initialized. If the movement path point corresponds to the temperature control influence area, the corresponding movement path point is initialized based on the second pheromone. The core high temperature area is used as the starting point of the ant colony. The heat path is simulated through the ant colony model, and regional heat prediction information is generated based on the simulated path. S7: Based on heat prediction information, perform heat distribution prediction and comprehensive temperature control analysis for multiple energy storage analysis areas, and generate regional temperature control schemes through a liquid-cooled temperature control platform; Specifically, S6 refers to: Based on the ant colony algorithm, in the energy storage model, the entire energy storage area is used as the movement range to construct the ant colony model, which is used to simulate the ant colony's running path. Each energy storage analysis area is used as a movement path point, and each movement path point has multiple movement options in different directions; Define a first pheromone and a second pheromone, where the value of the first pheromone is less than that of the second pheromone. Based on the first pheromone, initialize the pheromone of each movement path point; If the movement path point corresponds to the temperature control affected area, then the corresponding movement path point is initialized based on the second pheromone; A preset number of ant colonies is set, with the core high-temperature area as the starting point of the ant colonies. A preset number of ant colonies are set for each core high-temperature area. The ant colonies make path decisions through pheromones and heuristic information, and the path simulation is repeated until the preset number of simulations is reached. The termination condition for each path simulation is that the ant colony's movement path reaches the preset maximum length. Heat path simulation is performed based on ant colony model, corresponding movement paths are recorded, and high-frequency paths are selected from the movement paths as heat prediction simulation paths. Based on the heat prediction simulation path, regional mapping of heat trends is performed in the energy storage model to generate regional heat prediction information.

2. The method for analyzing liquid-cooled energy storage applications in multiple scenarios according to claim 1, characterized in that, Specifically, S1 is: Obtain information on the layout of energy storage areas; The energy storage area layout information includes the area of ​​the energy storage area, the area outline, the area equipment layout information, and the number of energy storage devices. A 3D visualization-based energy storage model is constructed based on the energy storage area layout information.

3. The method for analyzing liquid-cooled energy storage applications in multiple scenarios according to claim 1, characterized in that, Specifically, S2 is: The energy storage model is used to analyze the layout of energy storage devices, the distribution of energy storage units, and to divide the energy storage model into multiple energy storage analysis areas, ensuring that each energy storage analysis area includes a preset number of energy storage units.

4. The method for analyzing liquid-cooled energy storage applications in multiple scenarios according to claim 1, characterized in that, Specifically, S3 is: Set up energy storage task plans for multiple scenarios, and collect temperature control data from multiple energy storage analysis areas through a temperature control monitoring platform during the task plan period; Temperature control data includes temperature values ​​for the corresponding time period, liquid cooling equipment operation information, and temperature distribution information; The energy storage task plan includes the energy storage device discharge and charging task process; Based on the temperature control data, the corresponding temperature values ​​are sorted according to the time dimension, and the numerical change characteristics are calculated and analyzed based on linear change to generate temperature control change characteristics.

5. The method for analyzing liquid-cooled energy storage applications in multiple scenarios according to claim 4, characterized in that, Specifically, S4 is: The energy storage analysis area is used as the unit of analysis. The temperature control variation characteristics of an energy storage analysis area are marked as the first characteristic data; The temperature control change characteristics corresponding to multiple adjacent energy storage analysis areas in the one energy storage analysis area are marked as second feature data to obtain multiple second feature data; A linear correlation analysis based on the Pearson coefficient is performed between the first feature data and each second feature data to obtain multiple Pearson coefficients; Determine whether each Pearson coefficient is within the preset coefficient range. If so, it means that there is a high degree of influence between the energy storage analysis area and the corresponding adjacent energy storage analysis area. Count the number K of adjacent energy storage analysis areas with a high degree of influence. If K is greater than the predetermined quantity, then the energy storage analysis area will be marked as the temperature control influence area.

6. The method for analyzing liquid-cooled energy storage applications in multiple scenarios according to claim 5, characterized in that, Specifically, S5 is: Based on the current real-time energy storage plan, real-time temperature control data of multiple energy storage analysis areas are collected through the temperature control monitoring platform; Temperature change analysis is performed based on real-time temperature control data to calculate the temperature growth rate and maximum temperature value; Determine whether the temperature growth rate and maximum temperature value of each energy storage analysis area are both greater than the preset range. If so, mark the energy storage analysis area as the core high temperature area.

7. The method for analyzing liquid-cooled energy storage applications in multiple scenarios according to claim 1, characterized in that, Specifically, S7 is: Based on heat forecast information, analyze the distribution and trend of heat in each energy storage analysis area; Heat distribution prediction and comprehensive temperature control analysis are performed through a liquid-cooled temperature control platform, and a priority table and temperature control parameters for liquid-cooled temperature control are set for each energy storage analysis area. The application is based on the liquid cooling temperature control priority table and temperature control parameters to generate regional temperature control schemes.

8. A liquid-cooled energy storage application analysis system based on multiple scenarios, characterized in that, The system includes a memory and a processor. The memory includes a liquid-cooled energy storage application analysis program based on multiple scenarios. When the processor executes the liquid-cooled energy storage application analysis program based on multiple scenarios, it implements the steps of the liquid-cooled energy storage application analysis method based on multiple scenarios as described in claim 1.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a liquid-cooled energy storage application analysis program based on multiple scenarios. When the liquid-cooled energy storage application analysis program based on multiple scenarios is executed by a processor, it implements the steps of the liquid-cooled energy storage application analysis method based on multiple scenarios as described in any one of claims 1 to 7.

Images