Intelligent adjusting system for multi-stage cooling of energy storage system

Abstract

The invention provides an intelligent adjusting system for multi-stage cooling of an energy storage system, and the system comprises a multi-stage cooling module which comprises a plurality of different types of cooling units; the intelligent temperature monitoring module is used for collecting local temperature data of the energy storage system in real time, generating a temperature field distribution diagram according to the local temperature data, and determining a temperature abnormal area in the energy storage system based on the temperature field distribution diagram. The intelligent control module is used for determining the geometric center, the space range and the temperature extreme value of the temperature abnormal area as abnormal characteristics, outputting the abnormal grade of the temperature abnormal area based on the abnormal characteristics, and adjusting the operation state of the cooling unit according to the abnormal grade. The intelligent temperature monitoring module can collect local temperature data in real time and generate a temperature field distribution diagram, and temperature abnormal areas and features thereof are accurately identified. And the intelligent control module determines the abnormal level according to the abnormal characteristics and flexibly adjusts the running state of the cooling unit accordingly, so that the heat dissipation efficiency of the energy storage system is effectively improved, and the energy consumption is reduced.

Description

Technical Field

[0001] This invention relates to the field of energy storage system cooling technology, and in particular to an intelligent regulation system for multi-stage cooling of energy storage systems. Background Technology

[0002] With the rapid development of energy storage technology, energy storage systems have been widely used in power systems, renewable energy storage, and distributed energy management. Energy storage systems generate a large amount of heat during operation, especially during high-power charging and discharging or prolonged operation. Increased temperature can lead to performance degradation, shortened lifespan, and even safety issues for the energy storage equipment.

[0003] Therefore, effectively controlling the temperature of energy storage systems to ensure their safe and efficient operation within a specified temperature range is one of the major challenges currently facing energy storage technology. Traditional cooling systems typically employ a single cooling method, such as air cooling or liquid cooling. However, these methods often suffer from unsatisfactory cooling effects, high energy consumption, or slow response speeds when faced with complex operating conditions and varying temperature requirements. Summary of the Invention

[0004] This invention provides an intelligent regulation system for multi-stage cooling of an energy storage system, which solves the technical problems of insufficient accuracy in temperature field distribution monitoring and insufficient flexibility in cooling unit adjustment in existing cooling systems.

[0005] On one hand, the present invention provides an intelligent regulation system for multi-stage cooling of an energy storage system, comprising:

[0006] The multi-stage cooling module includes various types of cooling units; each cooling unit has a different cooling capacity and is used to regulate the temperature of the energy storage system.

[0007] The intelligent temperature monitoring module is used to collect local temperature data of the energy storage system in real time, generate a temperature field distribution map based on the local temperature data, and determine the temperature anomaly area in the energy storage system based on the temperature field distribution map.

[0008] The intelligent control module is used to determine the geometric center, spatial range, and temperature extreme values ​​of the temperature anomaly area as anomaly characteristics. Based on the anomaly characteristics, it outputs the anomaly level of the temperature anomaly area and adjusts the operating status of the cooling unit according to the anomaly level.

[0009] According to the present invention, a smart regulation system for multi-stage cooling of an energy storage system is provided, which determines the temperature anomaly region in the energy storage system based on a temperature field distribution map, including:

[0010] The temperature field distribution map is divided into regions, thereby dividing the energy storage system into multiple temperature sub-regions;

[0011] The average temperature, temperature standard deviation, and temperature gradient of each temperature sub-region are determined as the analysis results for the temperature sub-regions.

[0012] When the analysis results meet the preset abnormal conditions, the corresponding temperature sub-region is marked as a temperature abnormal region.

[0013] According to the present invention, a smart regulation system for multi-stage cooling of an energy storage system is provided, which determines the geometric center, spatial range, and temperature extreme values ​​of an abnormal temperature region, including:

[0014] Cluster analysis was performed on the temperature data of each temperature anomaly region to obtain the geometric center of each temperature anomaly region;

[0015] Determine the boundaries of each temperature anomaly region to obtain the spatial extent of each temperature anomaly region;

[0016] Within each temperature anomaly region, the highest temperature value is identified as the corresponding temperature extreme value.

[0017] According to the present invention, a smart regulation system for multi-stage cooling of an energy storage system performs cluster analysis on the temperature data of each temperature anomaly region to obtain the geometric center of each temperature anomaly region, including:

[0018] Extract the spatial coordinates of all temperature data points within the temperature anomaly area;

[0019] Calculate the mean of all spatial coordinates and use it as the geometric center;

[0020] Determine the boundaries of each temperature anomaly region to obtain the spatial extent of each temperature anomaly region, including:

[0021] Determine the distance from the geometric center to each data point within the temperature anomaly region;

[0022] Determine the maximum distance among all distances;

[0023] The spherical space is defined with the geometric center as the center and the maximum distance as the radius, which serves as the spatial range of the temperature anomaly region.

[0024] Within each temperature anomaly region, the highest temperature value is identified as the corresponding temperature extreme value, including:

[0025] Sort all temperature data points within the temperature anomaly area and find the highest temperature value;

[0026] When multiple identical maximum temperature values ​​exist, the spatial distribution distance between each location is determined based on the location of the identical maximum temperature value.

[0027] Based on the spatial distribution distance, the same highest temperature values ​​are clustered to obtain the clustered highest temperature values;

[0028] Select one of the highest temperature values ​​after clustering as the temperature extreme value of the temperature anomaly region.

[0029] According to the present invention, an intelligent regulation system for multi-stage cooling of an energy storage system marks the corresponding temperature sub-region as a temperature anomaly region when the analysis results meet preset anomaly conditions, including:

[0030] When the average temperature of a temperature sub-region continuously exceeds the first temperature threshold and reaches the first preset duration, the preset abnormal condition is met.

[0031] When the temperature standard deviation of a temperature sub-region is greater than the preset fluctuation threshold, the preset abnormal condition is met.

[0032] When the magnitude of the temperature gradient in a temperature sub-region is greater than the preset gradient threshold and the gradient direction points towards the core region inside the energy storage system, the preset abnormal condition is met.

[0033] According to the present invention, an intelligent regulation system for multi-stage cooling of an energy storage system outputs the anomaly level of an abnormal temperature region based on abnormal characteristics, including:

[0034] The radius of the spatial range and the extreme temperature values ​​are weighted and fused to obtain a comprehensive anomaly score;

[0035] Obtain the pre-created anomaly score ranges corresponding to multiple different anomaly levels;

[0036] The comprehensive anomaly score is mapped to the corresponding anomaly score range to output the corresponding anomaly level.

[0037] According to the present invention, an intelligent regulation system for multi-stage cooling of an energy storage system adjusts the operating state of the cooling unit based on the anomaly level, including:

[0038] Select the corresponding cooling unit group based on the anomaly level;

[0039] Determine the distance between each cooling unit in the cooling unit group and the geometric center of the temperature anomaly region;

[0040] In the cooling unit group, the cooling unit that is closest to the geometric center of the temperature anomaly area is selected as the target cooling unit;

[0041] Adjust the cooling intensity of the target cooling unit according to the anomaly level.

[0042] According to the present invention, an intelligent adjustment system for multi-stage cooling of an energy storage system adjusts the cooling intensity of a target cooling unit based on the anomaly level, comprising:

[0043] When the anomaly level is Level 1, control the target cooling unit to operate at rated power;

[0044] When the anomaly level is Level 2, the target cooling unit is controlled to operate at an enhanced power higher than the rated power;

[0045] When the anomaly level is level three, the target cooling unit and all cooling units in the same group are controlled to operate at the maximum combined cooling power, and directional focused cooling is performed on the geometric center of the temperature anomaly area corresponding to level three.

[0046] The degree of abnormality gradually increases from Level 1 to Level 2 and Level 3.

[0047] According to the present invention, a multi-stage cooling intelligent regulation system for an energy storage system generates a temperature field distribution map based on local temperature data, including:

[0048] Based on the location of each local temperature data within the energy storage system space, supplementary temperature data for areas within the energy storage system space where temperature sensors are not installed are determined;

[0049] The energy storage system space is divided into multiple grid cells;

[0050] The local temperature data and supplementary temperature data are assigned to the corresponding grid cells;

[0051] Mark the temperature value in each grid cell;

[0052] The temperature values ​​of all grid cells are visualized to generate a temperature field distribution map.

[0053] According to the present invention, a smart regulation system for multi-stage cooling of an energy storage system visualizes the temperature values ​​of all grid cells to generate a temperature field distribution map, including:

[0054] Map grid cells with different temperature values ​​to different colors;

[0055] Connecting grid cells with the same temperature value yields isotherms.

[0056] The intelligent regulation system for multi-stage cooling of energy storage systems provided by this invention achieves precise monitoring and intelligent regulation of the energy storage system temperature through the coordinated operation of multi-stage cooling modules, intelligent temperature monitoring modules, and intelligent control modules. The intelligent temperature monitoring module can collect local temperature data in real time and generate a temperature field distribution map, accurately identifying abnormal temperature areas and their characteristics. The intelligent control module determines the anomaly level based on the anomaly characteristics and flexibly adjusts the operating status of the cooling units accordingly, thereby effectively improving the heat dissipation efficiency of the energy storage system, reducing energy consumption, and enhancing the stability and safety of system operation. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of the structure of the intelligent regulation system for multi-stage cooling of the energy storage system provided in an embodiment of the present invention;

[0059] Figure 2 This is a flowchart illustrating the intelligent adjustment method for multi-stage cooling of an energy storage system provided in an embodiment of the present invention.

[0060] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0062] Figure 1 This is a schematic diagram of the structure of the intelligent regulation system for multi-stage cooling of the energy storage system provided in the embodiment of the present invention.

[0063] See Figure 1 The intelligent regulation system 100 for multi-stage cooling of the energy storage system includes a multi-stage cooling module 110, an intelligent temperature monitoring module 120, and an intelligent control module 130.

[0064] The multi-stage cooling module 110 includes various types of cooling units; each cooling unit has a different cooling capacity and is used to regulate the temperature of the energy storage system.

[0065] The intelligent temperature monitoring module 120 is used to collect local temperature data of the energy storage system in real time, generate a temperature field distribution map based on the local temperature data, and determine the temperature anomaly area in the energy storage system based on the temperature field distribution map.

[0066] The intelligent control module 130 is used to determine the geometric center, spatial range, and temperature extreme values ​​of the temperature anomaly area as anomaly characteristics. Based on the anomaly characteristics, it outputs the anomaly level of the temperature anomaly area and adjusts the operating status of the cooling unit according to the anomaly level.

[0067] In this embodiment, the cooling unit can be implemented using air cooling, liquid cooling, or phase change material cooling devices. For example, air cooling can be used in low-power scenarios, while liquid cooling can be switched to in high-power scenarios, mainly to achieve flexible matching for different temperature requirements. The local temperature data in the intelligent temperature monitoring module 120 can be obtained through temperature sensors deployed at key points in the energy storage system, or it can be collected non-contactly through infrared thermal imaging equipment. The purpose is to comprehensively capture the temperature change trend inside the energy storage system.

[0068] The intelligent regulation system 100 for multi-stage cooling of the energy storage system achieves precise monitoring and intelligent regulation of the system temperature through the coordinated operation of the multi-stage cooling module 110, the intelligent temperature monitoring module 120, and the intelligent control module 130. The intelligent temperature monitoring module 120 can collect local temperature data in real time and generate a temperature field distribution map, accurately identifying abnormal temperature areas and their characteristics. The intelligent control module 130 determines the anomaly level based on the anomaly characteristics and flexibly adjusts the operating status of the cooling units accordingly, thereby effectively improving the heat dissipation efficiency of the energy storage system, reducing energy consumption, and enhancing the stability and safety of system operation.

[0069] In one embodiment of this specification, determining the temperature anomaly region in an energy storage system based on a temperature field distribution map includes:

[0070] The temperature field distribution map is divided into regions, thereby dividing the energy storage system into multiple temperature sub-regions;

[0071] The average temperature, temperature standard deviation, and temperature gradient of each temperature sub-region are determined as the analysis results for the temperature sub-regions.

[0072] When the analysis results meet the preset abnormal conditions, the corresponding temperature sub-region is marked as a temperature abnormal region.

[0073] In this embodiment, the temperature field distribution map refers to a visual chart reflecting the spatial distribution of temperature generated by collecting local temperature data from the energy storage system. It can be implemented using a heat map, isotherm map, or three-dimensional temperature distribution model. The temperature standard deviation refers to the degree of dispersion of temperature data points within a temperature sub-region relative to the average temperature, and it is used to capture the anomalies of temperature fluctuations; the temperature gradient refers to the rate of change of temperature in space, and it is used to reflect the spatial trend of temperature distribution.

[0074] In this embodiment, the energy storage system is decomposed into multiple temperature sub-regions by dividing the temperature field distribution map into regions. This allows the system to focus on local temperature dynamics and avoids the global temperature averaging masking local hotspots. By calculating the average temperature, temperature standard deviation, and temperature gradient of each temperature sub-region, a multi-dimensional temperature feature analysis framework is constructed, effectively distinguishing normal temperature fluctuations from true anomaly areas. This judgment mechanism based on multi-dimensional parameter combinations significantly improves the accuracy of anomaly identification and provides a reliable basis for precise adjustment of the cooling system.

[0075] In one embodiment of this specification, determining the geometric center, spatial range, and temperature extremes of a temperature anomaly region includes:

[0076] Cluster analysis was performed on the temperature data of each temperature anomaly region to obtain the geometric center of each temperature anomaly region;

[0077] Determine the boundaries of each temperature anomaly region to obtain the spatial extent of each temperature anomaly region;

[0078] Within each temperature anomaly region, the highest temperature value is identified as the corresponding temperature extreme value.

[0079] In this embodiment, the temperature anomaly region refers to the area in the energy storage system that requires special attention due to localized overheating. This area can be divided and marked using data collected by the intelligent temperature monitoring module 120. Cluster analysis can be understood as an algorithmic processing method based on data distribution characteristics. Specifically, it can be implemented using methods such as K-means clustering and DBSCAN clustering. Its purpose is to accurately capture the core location of the anomaly region by analyzing the spatial clustering characteristics of temperature data. Determining the boundaries of each temperature anomaly region refers to defining the actual expansion range of the anomaly region based on temperature gradient changes or preset thresholds. This can be achieved using distance-based boundary detection methods or density-based boundary definition methods, thereby accurately reflecting the shape and size of the anomaly region. Temperature extreme values ​​refer to the most severe hotspot temperature values ​​within the temperature anomaly region. These can be achieved using sorting algorithms or maximum value extraction algorithms, aiming to directly locate the highest temperature point within the anomaly region and avoid misjudgments caused by ignoring local high-temperature points.

[0080] In this embodiment, cluster analysis is used to determine the geometric center, spatial range, and temperature extreme values ​​of the temperature anomaly region, further refining the characteristic description of the temperature anomaly region. This enables the system to more accurately locate the anomaly region, providing a more accurate basis for subsequent cooling strategy adjustments, thereby improving cooling efficiency and system safety.

[0081] In one embodiment of this specification, cluster analysis is performed on the temperature data of each temperature anomaly region to obtain the geometric center of each temperature anomaly region, including:

[0082] Extract the spatial coordinates of all temperature data points within the temperature anomaly area;

[0083] Calculate the mean of all spatial coordinates and use it as the geometric center;

[0084] Determine the boundaries of each temperature anomaly region to obtain the spatial extent of each temperature anomaly region, including:

[0085] Determine the distance from the geometric center to each data point within the temperature anomaly region;

[0086] Determine the maximum distance among all distances;

[0087] The spherical space is defined with the geometric center as the center and the maximum distance as the radius, which serves as the spatial range of the temperature anomaly region.

[0088] Within each temperature anomaly region, the highest temperature value is identified as the corresponding temperature extreme value, including:

[0089] Sort all temperature data points within the temperature anomaly area and find the highest temperature value;

[0090] When multiple identical maximum temperature values ​​exist, the spatial distribution distance between each location is determined based on the location of the identical maximum temperature value.

[0091] Based on the spatial distribution distance, the same highest temperature values ​​are clustered to obtain the clustered highest temperature values;

[0092] Select one of the highest temperature values ​​after clustering as the temperature extreme value of the temperature anomaly region.

[0093] This embodiment describes in detail how to determine the geometric center by calculating the mean of spatial location coordinates, how to determine the spatial range by the maximum distance, and how to determine the temperature extreme values ​​by sorting and clustering methods, thereby further improving the accuracy and reliability of abnormal region feature extraction and ensuring the precision and effectiveness of cooling regulation.

[0094] In this embodiment, the spatial definition of the temperature anomaly region is illustrated using a spherical space as an example, which is applicable to most scenarios where hotspots are concentrated. If the actual anomaly region is not spherical, ellipsoid fitting, polygon boundary recognition, or irregular shape description methods based on density clustering can be used. By adjusting the boundary determination algorithm, the system can dynamically adapt to anomaly regions of different shapes, ensuring the accuracy of the spatial description and the effectiveness of the cooling strategy.

[0095] In one embodiment of this specification, when the analysis results meet preset anomaly conditions, the corresponding temperature sub-region is marked as a temperature anomaly region, including:

[0096] When the average temperature of a temperature sub-region continuously exceeds the first temperature threshold and reaches the first preset duration, the preset abnormal condition is met.

[0097] When the temperature standard deviation of a temperature sub-region is greater than the preset fluctuation threshold, the preset abnormal condition is met.

[0098] When the magnitude of the temperature gradient in a temperature sub-region is greater than the preset gradient threshold and the gradient direction points towards the core region inside the energy storage system, the preset abnormal condition is met.

[0099] In this embodiment, the average temperature refers to the arithmetic mean obtained by statistically calculating temperature data over a certain time range. This can be achieved using a sliding window algorithm or a weighted average algorithm, aiming to capture the overall temperature trend and avoid interference from instantaneous fluctuations. The first temperature threshold can be an empirical value or a dynamically adjusted value set based on the heat resistance of the energy storage system's materials and operating conditions, ensuring that temperature anomaly detection is practically meaningful. The first preset duration refers to the length of time the temperature remains above the threshold. This can be set as a fixed value based on the thermal response characteristics of the energy storage system or as a variable dynamically adjusted based on environmental conditions, aiming to eliminate misjudgments caused by short-term temperature fluctuations. The core area inside the energy storage system refers to the concentrated area of ​​critical components that are most sensitive to overheating and where temperature runaway will directly lead to serious malfunctions (such as thermal runaway or permanent performance damage). This is typically the area inside the battery cell module, the battery management system, or critical areas such as cooling dead zones.

[0100] Specifically, the temperature standard deviation measures the dispersion of temperature distribution and can be calculated using statistical methods, such as the sample standard deviation formula or the population standard deviation formula. The preset fluctuation threshold can be a fixed value derived from historical data statistics or a parameter dynamically adjusted based on real-time operating conditions; its purpose is to identify potential risk areas with drastic temperature fluctuations. The magnitude of the temperature gradient refers to the rate of temperature change per unit distance in the temperature field, which can be calculated using the finite difference method or interpolation algorithms. The gradient direction can be determined through vector analysis, aiming to assess whether the spatial trend of heat transfer threatens the core area of ​​the energy storage system.

[0101] In this embodiment, by setting specific preset abnormal conditions, such as average temperature exceeding a threshold, temperature standard deviation greater than a threshold, and temperature gradient pointing to the core region, the system can more flexibly identify different types of temperature anomalies. This multi-condition judgment mechanism improves the accuracy and adaptability of anomaly detection and can better cope with complex temperature changes.

[0102] In one embodiment of this specification, based on abnormal characteristics, the abnormality level of a temperature abnormality region is output, including:

[0103] The radius of the spatial range and the extreme temperature values ​​are weighted and fused to obtain a comprehensive anomaly score;

[0104] Obtain the pre-created anomaly score ranges corresponding to multiple different anomaly levels;

[0105] The comprehensive anomaly score is mapped to the corresponding anomaly score range to output the corresponding anomaly level.

[0106] In this embodiment, the spatial extent refers to the physical coverage area of ​​the temperature anomaly region within the energy storage system, which can be defined by the maximum distance between the geometric center and the boundary. In practical applications, the radius of the spatial extent can be quantitatively described using a spherical or ellipsoidal model, aiming to reflect the impact of the anomaly region's diffusion degree on the risk of thermal runaway. The comprehensive anomaly score is a quantitative index generated by weighted fusion of the spatial extent's radius and temperature extreme values. Its weights can be flexibly adjusted according to actual needs, such as setting them based on empirical data or experimental results, with the aim of balancing the differences in the contribution of different characteristics to the severity of the anomaly. The anomaly scoring interval refers to the pre-defined scoring range for each anomaly level, which can be determined through statistical analysis of historical data or simulation results, with the aim of establishing a standardized grading framework.

[0107] In this embodiment, a comprehensive anomaly score is generated by weighting and fusing the radius of the spatial range and the extreme temperature values, and then mapping it to different anomaly levels. This enables a quantitative assessment of the temperature anomaly region, allowing the system to flexibly adjust the cooling strategy according to the severity of the anomaly, thereby improving the intelligence level of cooling regulation.

[0108] In one embodiment of this specification, adjusting the operating state of the cooling unit according to the anomaly level includes:

[0109] Select the corresponding cooling unit group based on the anomaly level;

[0110] Determine the distance between each cooling unit in the cooling unit group and the geometric center of the temperature anomaly region;

[0111] In the cooling unit group, the cooling unit that is closest to the geometric center of the temperature anomaly area is selected as the target cooling unit;

[0112] Adjust the cooling intensity of the target cooling unit according to the anomaly level.

[0113] In this embodiment, the anomaly level refers to the quantitative assessment result of the severity of the temperature anomaly region, which can be obtained through weighted fusion based on parameters such as the radius of the spatial range and the extreme temperature values. A cooling unit group refers to a group of cooling units with similar cooling capabilities or locational relationships, which can be divided according to the type, power, or arrangement of the cooling units. The geometric center of the temperature anomaly region is the center point obtained by averaging the spatial coordinates of all temperature data points within the region, used to characterize the core location of the anomaly region. The target cooling unit is the cooling unit in the cooling unit group that is closest to the geometric center; its selection aims to ensure that the cooling effect can quickly and directly act on the core part of the anomaly region.

[0114] In this embodiment, by selecting the corresponding cooling unit group according to the anomaly level and selecting the cooling unit closest to the geometric center of the anomaly area as the target cooling unit, the optimal configuration of cooling resources is achieved, which not only improves cooling efficiency but also reduces unnecessary energy consumption and enhances the economy and practicality of the system.

[0115] In one embodiment of this specification, adjusting the cooling intensity of the target cooling unit according to the anomaly level includes:

[0116] When the anomaly level is Level 1, control the target cooling unit to operate at rated power;

[0117] When the anomaly level is Level 2, the target cooling unit is controlled to operate at an enhanced power higher than the rated power;

[0118] When the anomaly level is level three, the target cooling unit and all cooling units in the same group are controlled to operate at the maximum combined cooling power, and directional focused cooling is performed on the geometric center of the temperature anomaly area corresponding to level three.

[0119] The degree of abnormality gradually increases from Level 1 to Level 2 and Level 3.

[0120] In this embodiment, directional focused cooling can be understood as a concentrated cooling operation targeting the geometric center of the temperature anomaly region. It achieves rapid heat removal by having multiple units collaboratively output maximum power and focus it on the hot spot area. A specific cooling strategy is provided by adjusting the cooling intensity of the target cooling unit according to the anomaly level. For example, the first level operates at rated power, the second level at enhanced power, and the third level at maximum combined cooling power with directional focused cooling. This allows for flexible adjustment of the cooling intensity based on the severity of the anomaly, ensuring efficient system operation and safety.

[0121] In one embodiment of this specification, generating a temperature field distribution map based on local temperature data includes:

[0122] Based on the location of each local temperature data within the energy storage system space, supplementary temperature data for areas within the energy storage system space where temperature sensors are not installed are determined;

[0123] The energy storage system space is divided into multiple grid cells;

[0124] The local temperature data and supplementary temperature data are assigned to the corresponding grid cells;

[0125] Mark the temperature value in each grid cell;

[0126] The temperature values ​​of all grid cells are visualized to generate a temperature field distribution map.

[0127] In this embodiment, supplementary temperature data refers to the temperature values ​​of areas without temperature sensors estimated through interpolation algorithms or other mathematical methods. Its purpose is to fill data gaps caused by uneven sensor distribution. In practical applications, interpolation algorithms can employ inverse distance weighted interpolation, Kriging interpolation, or polynomial fitting to ensure the accuracy and reliability of the supplementary temperature data. A grid cell refers to a regular region formed by discretizing the energy storage system space, providing a structured organizational framework for the temperature data. Specifically, the division of grid cells can be flexibly adjusted according to the geometry and size of the energy storage system, for example, using rectangular grids, hexagonal grids, or other suitable regular shapes.

[0128] In this embodiment, by generating a temperature field distribution map, including supplementing temperature data in areas without temperature sensors, dividing into grid cells, marking temperature values, and visualizing the data, a more comprehensive means of monitoring the temperature field is provided. This enables the system to understand the temperature distribution inside the energy storage system more intuitively, providing richer data support for intelligent regulation.

[0129] In one embodiment of this specification, the temperature values ​​of all grid cells are visualized to generate a temperature field distribution map, including:

[0130] Map grid cells with different temperature values ​​to different colors;

[0131] Connecting grid cells with the same temperature value yields isotherms.

[0132] In this embodiment, color mapping refers to the process of visually encoding grid cells based on changes in temperature values. Its purpose is to visually reflect the continuity of temperature distribution through a gradient color spectrum, thereby improving the operator's ability to quickly identify areas of abnormal temperature. Isotherms are closed or open curves formed by connecting grid cells with the same temperature value. They can be implemented using interpolation algorithms or spatial point tracking techniques, aiming to clearly delineate the boundary contours and spatial extent of areas of abnormal temperature.

[0133] In this embodiment, by mapping grid cells with different temperature values ​​to different colors and connecting grid cells with the same temperature value to generate isotherms, the visualization effect of the temperature field distribution map is further enhanced, enabling operators to observe the temperature distribution more intuitively, facilitating timely detection and handling of temperature anomalies, and improving the system's monitoring efficiency and user experience.

[0134] Based on the same general inventive concept, this invention also protects an intelligent regulation method for multi-stage cooling of an energy storage system, such as... Figure 2 As shown, Figure 2 This is a flowchart illustrating the intelligent regulation method for multi-stage cooling of an energy storage system provided in an embodiment of the present invention. The intelligent regulation method for multi-stage cooling of an energy storage system provided by the present invention will be described below. The intelligent regulation method for multi-stage cooling of an energy storage system described below can be referred to in correspondence with the intelligent regulation system for multi-stage cooling of an energy storage system described above.

[0135] The intelligent regulation method for multi-stage cooling of energy storage systems includes the following steps.

[0136] Step 201: Obtain various types of cooling units; each cooling unit has a different cooling capacity and is used to regulate the temperature of the energy storage system.

[0137] Step 202: Collect local temperature data of the energy storage system in real time, generate a temperature field distribution map based on the local temperature data, and determine the temperature anomaly area in the energy storage system based on the temperature field distribution map.

[0138] Step 203: Determine the geometric center, spatial range, and extreme temperature values ​​of the temperature anomaly region as anomaly characteristics. Based on the anomaly characteristics, output the anomaly level of the temperature anomaly region. Adjust the operating status of the cooling unit according to the anomaly level.

[0139] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0140] like Figure 3 As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logic instructions from the memory 330 to execute an intelligent adjustment method for multi-stage cooling of the energy storage system.

[0141] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, 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 steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0142] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the intelligent adjustment method for multi-stage cooling of the energy storage system provided by the above methods.

[0143] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an intelligent regulation method for multi-stage cooling of an energy storage system provided by the methods described above.

[0144] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0145] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent regulation system for multi-stage cooling of an energy storage system, characterized in that, include: A multi-stage cooling module includes various types of cooling units; each cooling unit has a different cooling capacity and is used to regulate the temperature of the energy storage system. The intelligent temperature monitoring module is used to collect local temperature data of the energy storage system in real time, generate a temperature field distribution map based on the local temperature data, and determine the temperature anomaly area in the energy storage system based on the temperature field distribution map. The intelligent control module is used to determine the geometric center, spatial range, and temperature extreme values ​​of the temperature anomaly region as anomaly characteristics, output the anomaly level of the temperature anomaly region based on the anomaly characteristics, and adjust the operating state of the cooling unit according to the anomaly level.

2. The intelligent regulation system for multi-stage cooling of the energy storage system according to claim 1, characterized in that, The step of determining the temperature anomaly region in the energy storage system based on the temperature field distribution map includes: The temperature field distribution map is divided into regions, thereby dividing the energy storage system into multiple temperature sub-regions; The average temperature, temperature standard deviation, and temperature gradient of each temperature sub-region are determined as the analysis results of the temperature sub-region. When the analysis results meet the preset abnormal conditions, the corresponding temperature sub-region is marked as a temperature abnormal region.

3. The intelligent regulation system for multi-stage cooling of the energy storage system according to claim 1, characterized in that, The process of determining the geometric center, spatial range, and temperature extremes of the temperature anomaly region includes: Cluster analysis was performed on the temperature data of each temperature anomaly region to obtain the geometric center of each temperature anomaly region; Determine the boundaries of each temperature anomaly region to obtain the spatial extent of each temperature anomaly region; Within each temperature anomaly region, the highest temperature value is identified as the corresponding temperature extreme value.

4. The intelligent regulation system for multi-stage cooling of the energy storage system according to claim 3, characterized in that, Cluster analysis was performed on the temperature data of each temperature anomaly region to obtain the geometric center of each temperature anomaly region, including: Extract the spatial coordinates of all temperature data points within the temperature anomaly area; Calculate the mean of the coordinates of all the aforementioned spatial locations, and use it as the geometric center; Determine the boundaries of each temperature anomaly region to obtain the spatial extent of each temperature anomaly region, including: Determine the distance from the geometric center to each data point within the temperature anomaly region; Determine the maximum distance among all distances; The spherical space is defined with the geometric center as the center and the maximum distance as the radius, which serves as the spatial range of the temperature anomaly region. Within each temperature anomaly region, the highest temperature value is identified as the corresponding temperature extreme value, including: Sort all temperature data points within the temperature anomaly area and find the highest temperature value; When multiple identical maximum temperature values ​​exist, the spatial distribution distance between each location is determined based on the location of the identical maximum temperature value. Based on the spatial distribution distance, the same highest temperature values ​​are clustered to obtain the clustered highest temperature values; Select one of the highest temperature values ​​after clustering as the temperature extreme value of the temperature anomaly region.

5. The intelligent regulation system for multi-stage cooling of the energy storage system according to claim 2, characterized in that, When the analysis results meet preset anomaly conditions, the corresponding temperature sub-region is marked as a temperature anomaly region, including: When the average temperature of the temperature sub-region continues to exceed the first temperature threshold and reaches the first preset duration, the preset abnormal condition is met. When the temperature standard deviation of the temperature sub-region is greater than the preset fluctuation threshold, the preset abnormal condition is met; When the magnitude of the temperature gradient in the temperature sub-region is greater than a preset gradient threshold and the gradient direction points to the core region inside the energy storage system, the preset abnormal condition is met.

6. The intelligent regulation system for multi-stage cooling of the energy storage system according to claim 4, characterized in that, The method of outputting the anomaly level of the temperature anomaly region based on the aforementioned anomaly characteristics includes: The radius of the spatial range and the extreme temperature values ​​are weighted and fused to obtain a comprehensive anomaly score; Obtain the pre-created anomaly score ranges corresponding to multiple different anomaly levels; The comprehensive anomaly score is mapped to the corresponding anomaly score range to output the corresponding anomaly level.

7. The intelligent regulation system for multi-stage cooling of the energy storage system according to claim 4, characterized in that, Adjusting the operating state of the cooling unit according to the abnormality level includes: Select the corresponding cooling unit group based on the aforementioned anomaly level; Determine the distance between each cooling unit in the cooling unit group and the geometric center of the temperature anomaly region; In the cooling unit group, the cooling unit that is closest to the geometric center of the temperature anomaly region is selected as the target cooling unit; Adjust the cooling intensity of the target cooling unit according to the level of the anomaly.

8. The intelligent regulation system for multi-stage cooling of the energy storage system according to claim 7, characterized in that, Adjusting the cooling intensity of the target cooling unit according to the anomaly level includes: When the anomaly level is the first level, the target cooling unit is controlled to operate at rated power; When the anomaly level is level two, the target cooling unit is controlled to operate at an enhanced power higher than the rated power; When the anomaly level is level three, the target cooling unit and all cooling units in the same group are controlled to operate at the maximum combined cooling power, and directional focused cooling is performed on the geometric center of the temperature anomaly area corresponding to level three. The degree of abnormality gradually increases from Level 1 to Level 2 and Level 3.

9. The intelligent regulation system for multi-stage cooling of the energy storage system according to claim 1, characterized in that, The step of generating a temperature field distribution map based on the local temperature data includes: Based on the location of each local temperature data within the energy storage system space, supplementary temperature data for areas within the energy storage system space where no temperature sensors are installed are determined. The space of the energy storage system is divided into multiple grid cells; The local temperature data and the supplementary temperature data are assigned to the corresponding grid cells; Mark the temperature value in each grid cell; The temperature values ​​of all grid cells are visualized to generate a temperature field distribution map.

10. The intelligent regulation system for multi-stage cooling of an energy storage system according to claim 9, characterized in that, The temperature values ​​of all grid cells are visualized to generate a temperature field distribution map, including: Map grid cells with different temperature values ​​to different colors; Connecting grid cells with the same temperature value yields isotherms.

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