Adaptive heat dissipation method and system for modular battery modules

Abstract

The present invention discloses an adaptive heat dissipation method and system for modular battery modules, which relates to the field of intelligent control technology. The method includes: real-time collection of working environment monitoring information of a target battery module of deep-sea exploration equipment, and matching to obtain an optimal dynamic heat dissipation mode; dividing the target battery module into multiple independent heat dissipation modules, building a modular battery heat dissipation architecture, real-time collection of working status data of the target battery module for hot spot distribution analysis, generating a module temperature gradient, and combining the modular battery heat dissipation architecture with the optimal dynamic heat dissipation mode to perform stepped layered heat dissipation control. The present invention solves the technical problem that existing battery modules cannot effectively cope with the high pressure, low temperature and corrosive conditions in deep-sea environments, resulting in low heat dissipation efficiency and reduced battery performance. It achieves the technical effect of improving the heat dissipation efficiency and performance of battery modules in deep-sea environments through a modular battery heat dissipation architecture and a stepped layered dynamic heat dissipation strategy.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an adaptive heat dissipation method and system for a modular battery module. Background Art

[0002] Deep-sea exploration equipment (such as unmanned submersibles and seabed observatories) needs to operate stably for long periods of time in high-pressure, low-temperature, and seawater-corrosive environments. However, the deep-sea environment has extreme characteristics such as high pressure, low temperature, and seawater corrosion, which poses many challenges to traditional battery cooling systems. In existing technologies, most cooling systems rely on air cooling or liquid cooling, which cannot provide effective heat dissipation support in deep-sea environments. In addition, deep-sea exploration equipment usually operates in a changing environment, and the heat dissipation requirements of battery modules vary with factors such as depth, pressure, and temperature. Existing cooling systems cannot be adjusted intelligently and often suffer from problems such as low heat dissipation efficiency, resource waste, and battery overheating. Summary of the Invention

[0003] This application provides an adaptive heat dissipation method and system for modular battery modules, which is used to solve the technical problem that existing battery modules cannot effectively cope with the high pressure, low temperature and corrosive conditions in deep-sea environments, resulting in low heat dissipation efficiency and decreased battery performance.

[0004] The first aspect of the present application provides an adaptive heat dissipation method for a modular battery module, the method comprising: real-time collection of working environment monitoring information of a target battery module, the target battery module being applied to deep-sea exploration equipment; based on the working environment monitoring information, matching and obtaining an optimal dynamic heat dissipation mode, wherein the optimal dynamic heat dissipation mode corresponds to a variety of battery working environment scenarios; dividing the target battery module into multiple independent heat dissipation modules, and building a modular battery heat dissipation architecture based on the multiple independent heat dissipation modules, wherein each heat dissipation module is configured with a corresponding independent heat dissipation unit; based on the independent heat dissipation unit, real-time collection of working status data of the target battery module, and performing hotspot distribution analysis based on the working status data to generate a module temperature gradient; based on the modular battery heat dissipation architecture, performing step-by-step layered heat dissipation control according to the module temperature gradient and the optimal dynamic heat dissipation mode.

[0005] The second aspect of the present application provides an adaptive heat dissipation system for a modular battery module, the system comprising: a working environment monitoring module, the working environment monitoring module being used to collect working environment monitoring information of a target battery module in real time, the target battery module being applied to deep-sea exploration equipment; a dynamic heat dissipation mode matching module, the dynamic heat dissipation mode matching module being used to match and obtain an optimal dynamic heat dissipation mode based on the working environment monitoring information, wherein the optimal dynamic heat dissipation mode corresponds to a variety of battery working environment scenarios; a battery heat dissipation architecture building module, the battery heat dissipation architecture building module being used to divide the target battery module into multiple independent heat dissipation modules, and to build a modular battery heat dissipation architecture based on the multiple independent heat dissipation modules, wherein each heat dissipation module is configured with a corresponding independent heat dissipation unit; a hotspot distribution analysis module, the hotspot distribution analysis module being used to collect working status data of the target battery module in real time based on the independent heat dissipation unit, and to perform hotspot distribution analysis based on the working status data to generate a module temperature gradient; and a stepped layered heat dissipation control module, the stepped layered heat dissipation control module being used to perform stepped layered heat dissipation control according to the module temperature gradient and the optimal dynamic heat dissipation mode based on the modular battery heat dissipation architecture.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The adaptive heat dissipation method and system for modular battery modules provided in the present application relate to the field of intelligent control technology. By real-time collection of working environment information of the battery module of deep-sea exploration equipment, matching the optimal dynamic heat dissipation mode, and dividing it into multiple independent heat dissipation modules, each module is equipped with an independent heat dissipation unit. The hot spot distribution is analyzed according to the working status data, a temperature gradient is generated, and a stepped layered heat dissipation control is implemented based on the modular architecture. This solves the technical problem that the existing battery modules cannot effectively cope with the high pressure, low temperature and corrosive conditions in the deep-sea environment, resulting in low heat dissipation efficiency and decreased battery performance. It achieves the technical effect of improving the heat dissipation efficiency and performance of the battery module in the deep-sea environment through a modular battery heat dissipation architecture and a stepped layered dynamic heat dissipation strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of an adaptive heat dissipation method for a modular battery module provided in an embodiment of the present application;

[0010] Figure 2 Schematic diagram of the adaptive heat dissipation system structure of the modular battery module provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: working environment monitoring module 11 , dynamic heat dissipation mode matching module 12 , battery heat dissipation architecture building module 13 , hotspot distribution analysis module 14 , stepped layered heat dissipation control module 15 . DETAILED DESCRIPTION

[0012] This application provides an adaptive heat dissipation method and system for modular battery modules, which is used to solve the technical problem that existing battery modules cannot effectively cope with the high pressure, low temperature and corrosive conditions in deep-sea environments, resulting in low heat dissipation efficiency and decreased battery performance.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Example 1, as Figure 1 As shown, the present application provides an adaptive heat dissipation method for a modular battery module, the method comprising:

[0016] P10: Real-time collection of working environment monitoring information of a target battery module, where the target battery module is used in deep-sea exploration equipment.

[0017] Specifically, it is first necessary to collect real-time monitoring information on the working environment of the target battery module. This information collection process is the basis for ensuring that the battery module can operate effectively in deep-sea exploration equipment. The deep-sea environment has extreme temperatures, pressures, and the corrosiveness of seawater. Real-time monitoring of changes in these factors can provide important data support for subsequent heat dissipation control. Specifically, the target battery module is used in deep-sea exploration equipment, which means that the working environment of the equipment may include extremely low temperatures (usually below 0°C) and extremely high pressures (which can reach pressures of hundreds to thousands of meters deep), as well as the corrosive characteristics of seawater. Therefore, the collected working environment information includes not only ambient temperature and water depth, but also multiple environmental variables such as seawater pressure, salinity, humidity, and flow rate. These factors jointly affect the performance of the battery module.

[0018] To achieve this, multiple high-precision environmental monitoring sensors must be installed around and within the target battery module. These sensors may include temperature, pressure, humidity, current, and voltage sensors. These sensors provide real-time feedback on environmental changes. For example, temperature sensors can accurately monitor the temperature difference between the battery module and the surrounding environment, while pressure sensors measure the impact of deep-sea pressure fluctuations on the battery.

[0019] This multi-dimensional, multi-level data collection ensures complete information on the battery module's operating status under various environmental conditions, providing a data basis for subsequent intelligent heat dissipation control and ensuring that the battery module maintains optimal performance during deep-sea exploration. This process is crucial for real-time system adjustments and emergency response, effectively preventing equipment failure or performance degradation due to environmental changes.

[0020] P20: Based on the working environment monitoring information, an optimal dynamic heat dissipation mode is matched, wherein the optimal dynamic heat dissipation mode corresponds to multiple battery working environment scenarios.

[0021] Furthermore, step P20 in this embodiment of the present application further includes:

[0022] P21: Extract the target deep-sea environmental characteristics based on the working area location of the deep-sea exploration equipment; P22: Cluster the target deep-sea environmental characteristics to generate multiple dynamic heat dissipation modes, and establish a dynamic heat dissipation mode library based on them; P23: According to the working environment monitoring information, traverse the dynamic heat dissipation mode library to match the optimal dynamic heat dissipation mode.

[0023] It should be understood that by processing the aforementioned working environment monitoring information, the optimal dynamic heat dissipation mode is intelligently matched to the different working environment scenarios of the battery module to ensure that the battery maintains good heat dissipation in various deep-sea exploration scenarios, thereby extending the service life of the battery module and maintaining its stability and efficiency. The optimal dynamic heat dissipation mode refers to adjusting the operating mode of the heat dissipation system based on changing factors in the deep-sea environment (such as temperature and pressure) and the operating state of the battery module (such as load, temperature, power, etc.) to achieve the most ideal heat dissipation effect. This mode is selected based on factors such as ambient temperature, operating depth, and seawater pressure, and overheating or overcooling is avoided by precisely matching the corresponding heat dissipation method.

[0024] Specifically, we first extract the relevant deep-sea environmental characteristics based on the device's specific deep-sea location. Deep-sea exploration equipment typically operates in different sea areas, where environmental characteristics (such as seawater temperature, salinity, and pressure) can vary significantly. By analyzing the target device's operating area (such as water depth and geographic location), we extract environmental data characteristics relevant to that location. For example, in shallower waters, temperatures are higher and pressures are lower, while in deeper waters, temperatures are lower and pressures are higher. This data helps to subsequently select the optimal cooling mode.

[0025] Next, based on the target deep-sea environmental characteristics extracted from different working areas, similar environmental conditions are grouped together through cluster analysis, generating a variety of dynamic heat dissipation modes for different environments. For example, when the deep-sea temperature is high, an enhanced heat dissipation mode in a high-temperature environment can be selected, while in a low-temperature environment, a lower-level heat dissipation mode can be selected. By clustering environmental characteristics, a dynamic heat dissipation mode library is established. Each heat dissipation mode corresponds to a specific environmental scenario, and the overall heat dissipation basic mode can be determined for different environmental scenarios. The cluster analysis method may adopt algorithms such as K-means clustering and hierarchical clustering to divide the environmental characteristic data into several different categories based on their similarity, providing multiple options for subsequent matching of heat dissipation modes.

[0026] Next, based on real-time operating environment monitoring information, the system traverses the established dynamic cooling mode library and selects the cooling mode that best suits the current environmental conditions, taking into account the battery module's current operating status (such as load, battery temperature, and ambient temperature) and environmental data. By matching this mode with the preset modes in the mode library, the system rapidly responds to environmental changes and promptly adjusts the cooling system's operating mode. This matching process can be implemented using algorithms, such as similarity matching or nearest neighbor search, to filter various cooling strategies in the mode library based on real-time monitored environmental variables and select the most appropriate cooling mode. This process enables deep-sea exploration equipment to flexibly adapt to various complex deep-sea environments and ensure the continued efficient operation of the battery modules.

[0027] Through the above steps, the entire system can intelligently select the optimal dynamic heat dissipation mode based on the actual working environment of the deep-sea exploration equipment, and effectively manage the heat dissipation requirements of the battery module, avoiding equipment failures caused by improper heat dissipation, and improving the reliability and performance of deep-sea exploration equipment.

[0028] P30: Divide the target battery module into multiple independent heat dissipation modules, and build a modular battery heat dissipation architecture based on the multiple independent heat dissipation modules, wherein each heat dissipation module is configured with a corresponding independent heat dissipation unit.

[0029] Optionally, to meet the heat dissipation requirements of the target battery module in deep-sea exploration equipment, the battery module can be divided into multiple independent heat dissipation modules, that is, the entire battery module can be divided into multiple modular heat dissipation units, each unit working independently, so as to perform temperature control management more accurately and efficiently, and avoid the overall performance degradation of the heat dissipation system due to overheating of a single module.

[0030] Specifically, the target battery module is rationally divided into multiple independent cooling modules based on the battery's structural characteristics and the distribution of heat generation. This division aims to better address the varying temperature gradients generated in different areas of the battery module and avoid the limitations of over-reliance on a single cooling method. Different parts of the battery module may generate different amounts of heat during operation due to varying loads. The modular design allows for more precise heat dissipation in hot spots.

[0031] Each heat dissipation module is equipped with an independent heat dissipation unit, including heat sinks, heat pipes, coolant circuits, and other heat dissipation components. Each independent heat dissipation unit can autonomously adjust according to its operating area, ensuring that the temperature control in that area is always optimal. For example, in areas where the battery module has a high load and generates a lot of heat, the heat dissipation module can be equipped with more efficient heat dissipation equipment, such as high-thermal conductivity heat sinks or liquid cooling pipes. In areas with lower loads and more stable temperatures, simpler heat dissipation methods can be used.

[0032] This modular design allows the battery modules to flexibly adapt to the cooling requirements of deep-sea exploration equipment in varying operating environments and conditions. In deep-sea environments, where pressure and temperature fluctuate dramatically, the battery modules may need to frequently adjust their cooling methods. This modular design provides the entire system with greater adaptability and flexibility, enabling each cooling module to adjust its cooling method based on real-time operating conditions, ensuring overall stable operation of the equipment.

[0033] In addition, the design of the modular battery heat dissipation architecture can also enable each module to perform fine-grained temperature control adjustments for specific areas through independent heat dissipation modules and units, avoiding uneven heat dissipation of the overall system and improving heat dissipation efficiency. Secondly, the modular design makes the system more scalable and maintainable. If a part of the heat dissipation module fails or needs to be upgraded, the operation of other modules will not be affected, facilitating subsequent repairs and upgrades. Finally, considering the large differences in temperature and pressure in different deep-sea environments, the modular design can adjust the configuration and working mode of the heat dissipation module according to different deep-sea exploration scenarios, ensuring the best heat dissipation effect in any environment, and providing strong guarantees for the stable operation of deep-sea exploration equipment in harsh environments.

[0034] P40: Based on the independent heat dissipation unit, the operating status data of the target battery module is collected in real time, and hot spot distribution analysis is performed based on the operating status data to generate a module temperature gradient.

[0035] Furthermore, step P40 in this embodiment of the present application further includes:

[0036] P41: Obtain the spatial arrangement information of the target battery module, perform spatial positioning, and generate a spatial coordinate code for each independent battery module; P42: Based on the working status data, obtain the operating temperature information of each independent battery module; P43: Based on the operating temperature information of each independent battery module, combine the spatial coordinate code to generate a spatial thermal distribution map; P44: Based on the spatial thermal distribution map, perform hotspot distribution analysis to generate the module temperature gradient.

[0037] It should be understood that based on the independent heat dissipation unit, the working status data of the target battery module is collected in real time, and the temperature distribution of the battery module is analyzed in detail based on this data to generate the temperature gradient of the module, so as to provide an accurate basis for subsequent heat dissipation control.

[0038] First, obtain the spatial arrangement information of the target battery module. In deep-sea exploration equipment, the target battery module contains multiple independent battery cells, which are arranged in a specific manner in the equipment. To ensure that the subsequent temperature distribution analysis can be carried out accurately, it is necessary to first obtain the spatial arrangement information of the battery module. Spatial arrangement information refers to the position and layout of each battery cell in the battery module in three-dimensional space, which can reflect the spatial relationship between each module. This information can be obtained through design drawings, three-dimensional modeling, or sensor positioning systems. Based on this information, a spatial coordinate code is generated for each independent battery module. This code is a unique identifier that indicates the precise position of each battery module in space. This encoding method facilitates tracking and mapping the position of each individual battery cell in subsequent analysis, ensuring that temperature data can be accurately located for each battery module.

[0039] Next, based on the collected operating status data, the operating temperature information of each individual battery module is obtained. During this process, temperature sensors and operating status monitoring equipment can be used to monitor the operating status of the battery module in real time. Precision sensors collect the operating status data of the battery module, mainly including data such as the battery cell voltage, current, load, and ambient temperature. This data helps the system predict the temperature rise of each battery cell. Battery temperature typically rises, especially under high load or during charging. By collecting this temperature data, a comprehensive understanding of the temperature change trend of the battery module can be obtained. For each battery cell, its real-time operating temperature is recorded and compared with the data of other battery cells to ensure that the temperature of each cell is accurately recorded. This temperature information can then be used to formulate more precise heat dissipation measures.

[0040] Next, the operating temperature information of each independent battery module is used in combination with the spatial coordinate code to generate a spatial thermal distribution map. This process first matches the operating temperature of the battery module with the spatial coordinate code, making it easier to map the temperature data to the actual physical space of the battery module. That is, the operating temperature information of each battery cell is mapped to its corresponding spatial coordinates, thereby generating a temperature distribution map in three-dimensional space. The spatial thermal distribution map will show the temperature differences in different areas of the battery module, allowing users to intuitively see which areas have higher temperatures and which areas have lower temperatures. Through the display of the heat map, the system can identify areas that may be at risk of overheating, as well as areas with lower temperatures, providing strong data support for subsequent temperature control and heat dissipation strategies.

[0041] Finally, based on the generated spatial thermal distribution map, the system performs hotspot distribution analysis and then generates a module temperature gradient. The core goal of hotspot distribution analysis is to find the areas with the highest temperature in the battery module. These areas usually require special attention and more efficient heat dissipation measures. When performing hotspot distribution analysis, image processing or data analysis algorithms can be used to identify hotspot areas in the spatial thermal distribution map and determine the specific temperature values of these areas. The temperature gradient of the module is generated by calculating the difference between the hotspot area and the surrounding temperature. The temperature gradient represents the temperature difference between different locations inside the battery module. It can help the system identify which parts need priority heat dissipation and which parts have relatively low temperature control requirements. It helps to optimize the heat dissipation design of the battery module and ensure the long-term and stable operation of deep-sea exploration equipment in various complex environments.

[0042] Furthermore, step P44 of the embodiment of the present application further includes:

[0043] P44-1: Based on the spatial thermal distribution map, multiple temperature diffusion center points are extracted; P44-2: For the multiple temperature diffusion center points, a neighborhood temperature diffusion trend analysis is performed, and multiple temperature control areas are divided according to the temperature diffusion trend; P44-3: Based on the multiple temperature control areas, the temperature change data sequence of each independent heat dissipation module is extracted in an associated manner; P44-4: Based on the temperature change data sequence, the temperature change direction of each area is determined, and multiple temperature change vectors are generated in combination with the center point temperatures of the multiple temperature diffusion center points; P44-5: Based on the multiple temperature change vectors, the heat dissipation priority is sorted to generate the module temperature gradient.

[0044] Optionally, the temperature gradient generation process can be further refined to ensure that the heat dissipation measures in each temperature control area can accurately meet the working requirements of the battery module.

[0045] First, based on the spatial thermal distribution map, multiple temperature diffusion centers are extracted. In the generated spatial thermal distribution map, certain areas have higher temperatures. These areas are typically where heat is concentrated and may lead to overheating. Therefore, an algorithm is used to detect temperature peaks or hot spots in the spatial thermal distribution map and extract multiple temperature diffusion centers from the thermal map. These centers represent the core areas of heat propagation and the starting points of heat diffusion. Each temperature diffusion center typically has a higher temperature value, and the surrounding areas are affected by the heat diffusion.

[0046] Next, a neighborhood temperature diffusion trend analysis is performed for multiple temperature diffusion center points, and multiple temperature control areas are divided based on the temperature diffusion trends. By analyzing the temperature change trends in the areas surrounding multiple temperature diffusion center points, the trend of heat diffusion in the surrounding areas can be determined. For example, a data processing algorithm can be used to analyze the temperature changes in the neighborhood of each temperature diffusion center point to predict the heat diffusion trend. Based on these temperature diffusion trends, the heat diffusion range is divided into multiple temperature control areas. Areas with more concentrated heat diffusion will be divided into an independent temperature control area, while areas with more stable temperature changes will form another area.

[0047] On this basis, taking the multiple temperature control areas as the benchmark, the temperature change data series of each independent heat dissipation module is correlated and extracted, that is, the temperature change data series within a period of time. By correlating these data, the system can understand in detail the temperature change trend of each heat dissipation module within the temperature control area.

[0048] Next, based on the temperature change data sequence, the direction of temperature change in each area—that is, whether the temperature is rising or falling—is determined. This process is achieved by calculating parameters such as the rate and direction of temperature change. Based on this, multiple temperature change vectors are generated, combining the center point temperature of each temperature diffusion center point. The temperature change vectors represent the direction and intensity of temperature change, providing a quantitative basis for subsequent cooling decisions. Each vector indicates the trend and speed of temperature change in a particular area and is a component of the temperature gradient.

[0049] Finally, after generating the temperature change vector, a priority sorting algorithm determines the cooling priority for each region based on the rate and direction of temperature change. Regions with faster temperature changes or rising temperatures are assigned higher cooling priority, allowing timely cooling measures to prevent overheating. Regions with slower temperature changes or falling temperatures are assigned lower cooling priority. This cooling priority ranking ensures that the areas most in need of cooling are prioritized during the battery module cooling process, maximizing cooling effectiveness and preventing overheating from impacting battery performance.

[0050] This series of steps not only accurately identifies the temperature trends of each temperature-controlled area, but also generates a scientific and reasonable heat dissipation strategy based on actual temperature control requirements. Heat dissipation priorities are then prioritized to ensure that each area's heat dissipation needs are promptly and effectively addressed. The resulting module temperature gradient provides clear guidance for the battery module's heat dissipation control system, ensuring continued stable operation in the complex deep-sea exploration environment.

[0051] P50: Based on the modular battery heat dissipation architecture, step-by-step layered heat dissipation control is performed according to the module temperature gradient and the optimal dynamic heat dissipation mode.

[0052] Furthermore, step P50 in the embodiment of the present application further includes:

[0053] P51: Determine the hierarchical heat dissipation priority based on the module temperature gradient, and the hierarchical heat dissipation priority includes the heat dissipation priority identification of each independent battery module; P52: Based on the dynamic heat dissipation mode, match the initial heat dissipation strategy, and generate the optimal heat dissipation strategy based on the hierarchical heat dissipation priority and perform hierarchical subdivision optimization; P53: Execute the stepped hierarchical heat dissipation control of the optimal heat dissipation strategy according to the modular battery heat dissipation architecture.

[0054] Specifically, based on the modular battery cooling architecture, step-by-step layered cooling control is performed according to the module temperature gradient and the optimal dynamic cooling mode to ensure that each area can obtain appropriate cooling measures according to its temperature conditions to achieve efficient cooling effects and maintain long-term stable operation of the equipment.

[0055] First, based on the module temperature gradient, the system extracts the hierarchical cooling priority of each independent cooling module. This means analyzing the module temperature gradient to determine the cooling level of each independent cooling module and calibrating the priority to create a cooling priority identifier for each independent battery module. This ensures that during the battery module cooling process, higher-temperature or higher-priority areas are prioritized, preventing battery performance degradation or damage due to overheating.

[0056] Next, based on the dynamic cooling mode, the system searches through a library of basic cooling strategies to find an initial cooling strategy. This library corresponds to the dynamic cooling mode library and contains benchmark cooling strategies for various battery operating scenarios. These strategies can be trained and constructed based on empirical data. The initial cooling strategy provides basic cooling configuration for each individual battery module, such as determining the intensity and range of cooling.

[0057] Subsequently, the initial heat dissipation strategy is subdivided and optimized according to the determined hierarchical heat dissipation priority. For example, a corresponding reinforcement coefficient is generated according to the heat dissipation priority level, and the reinforcement coefficient is used to tune the parameters of the sub-strategies of each independent battery module of the initial heat dissipation strategy, thereby optimizing resource allocation and heat dissipation efficiency. For example, for high-priority areas, more heat dissipation resources or a higher heat dissipation order can be allocated. For low-priority areas, a relatively mild heat dissipation method is adopted. Through such subdivision optimization, the heat dissipation requirements of each area are precisely controlled, and resources are ensured to be effectively utilized to avoid unnecessary energy waste due to excessive heat dissipation.

[0058] Finally, based on the modular battery cooling architecture, the stepped hierarchical cooling control of the optimal cooling strategy is executed. This step is the actual execution stage of the cooling control, which aims to ensure that each battery module area performs cooling operations according to the cooling priority and optimization strategy. The core of the stepped hierarchical cooling control is to start different cooling measures in sequence according to the cooling priority of each area. In areas with higher priority, strong cooling measures are enabled, such as increasing the flow rate of the liquid cooling circuit or starting more cooling units; for areas with lower priority, the cooling measures may be more moderate, and temperature control may be maintained only through natural convection or lower-power cooling units.

[0059] Stepped control not only ensures differentiated cooling within different temperature control zones, but also avoids unnecessary energy consumption and reduces operating costs. Each cooling module performs cooling control based on its own priorities and needs, ensuring precise temperature regulation across the entire battery module.

[0060] Furthermore, step P52 of the embodiment of the present application further includes:

[0061] P52-1: Based on the dynamic heat dissipation mode, traverse the basic heat dissipation strategy library to match the initial heat dissipation strategy, and the initial heat dissipation strategy includes the basic operating parameters of each independent heat dissipation unit; P52-2: According to the hierarchical heat dissipation priority, perform first-order strategy optimization on the initial heat dissipation strategy to generate a first-order heat dissipation strategy; P52-3: Combined with the spatial arrangement information of the target battery module, perform heat dissipation interference analysis to generate multiple field interference coefficients; P52-4: Based on the multiple field interference coefficients, perform second-order strategy optimization on the first-order heat dissipation strategy to generate the optimal heat dissipation strategy.

[0062] It should be understood that the process of generating the optimal heat dissipation strategy can be further refined to ensure that each heat dissipation unit can be flexibly adjusted according to the priority and actual working environment, thereby achieving the best heat dissipation effect.

[0063] First, based on the dynamic heat dissipation mode, the basic heat dissipation strategy library is traversed to match the initial heat dissipation strategy. In this step, the system will traverse the established basic heat dissipation strategy library based on the real-time working environment conditions and the current state of the target battery module, and select the most appropriate initial heat dissipation strategy from it. The basic heat dissipation strategy library contains a variety of preset heat dissipation solutions, each of which corresponds to a specific working scenario and environmental conditions. The initial heat dissipation strategy is selected according to the current dynamic heat dissipation mode (such as temperature, pressure, load, etc.). These strategies include the basic operating parameters of each independent heat dissipation unit, such as coolant flow rate, heat sink operating frequency, fan speed, etc. By matching the appropriate initial strategy, a basic solution framework is provided for the heat dissipation of the battery module.

[0064] Then, based on the hierarchical heat dissipation priority, the initial heat dissipation strategy is optimized in a first-order strategy. The purpose of the first-order strategy optimization is to refine the initial heat dissipation strategy according to the heat dissipation requirements of different areas, ensuring that areas with higher temperatures obtain more heat dissipation resources and a higher heat dissipation order, while low-temperature areas relatively reduce heat dissipation resources. For example, high-temperature areas may increase the flow of the liquid cooling system or start more heat dissipation units, while low-temperature areas may maintain a lower heat dissipation intensity. Through this optimization, the system can more accurately adjust the heat dissipation strategy according to the heat dissipation priority, avoid unnecessary waste of resources, and improve heat dissipation efficiency.

[0065] Next, combined with the spatial arrangement information of the target battery module, a heat dissipation interference analysis is performed to generate multiple field interference coefficients. The core of this step is to identify the mutual interference between the heat dissipation units inside the battery module. In the modular battery heat dissipation architecture, there may be interference effects of heat transfer between different heat dissipation units, especially in denser battery arrangements. This interference may affect the heat dissipation efficiency. The system will analyze the relative positions of the various heat dissipation units and the heat transfer effects between them in combination with the spatial arrangement information of the battery module. By analyzing these interference effects, multiple field interference coefficients are generated, which reflect the degree of thermal interference between the heat dissipation units.

[0066] Finally, based on the multiple domain interference coefficients, the first-order heat dissipation strategy is optimized for a second-order strategy. The goal of the second-order strategy optimization is to eliminate or reduce the interference between the heat dissipation units to ensure that each area can dissipate heat efficiently. The heat dissipation parameters of the domain battery module can be adjusted according to the size and direction of the domain interference coefficient. For example, if the temperature of the neighboring battery module of any independent battery module is too high, which has a negative impact on the heat dissipation of the independent battery module, the heat dissipation power of the independent battery module can be enhanced by the domain interference coefficient to reduce the interference effect.

[0067] Through these steps, the initial cooling strategy is optimized to ensure that each independent cooling unit can flexibly adjust the cooling control according to actual needs and space layout to achieve more effective cooling management.

[0068] Furthermore, before executing the stepped hierarchical heat dissipation control of the optimal heat dissipation strategy, the embodiment of the present application further includes step P53a, and step P53a further includes:

[0069] P53-1a: Receive the working status data of the target battery module and extract multidimensional feature parameters based on the working status data; P53-2a: Evaluate the working status of the battery module based on the multidimensional feature parameters, including evaluating the battery health status, temperature distribution uniformity and heat dissipation efficiency, and generate multivariate evaluation indicators; P53-3a: Based on the multivariate evaluation indicators, construct a fine-tuning function according to the indicator priority, and perform reinforcement learning through the fine-tuning function to generate a multivariate correction vector; P53-4a: Based on the multivariate correction vector, correct the optimal heat dissipation strategy.

[0070] In a possible embodiment of the present application, before executing the stepped hierarchical heat dissipation control of the optimal heat dissipation strategy, the optimal heat dissipation strategy may be refined according to the working state of the target battery module.

[0071] First, the real-time working status data of the target battery module is received. These data include key parameters such as the voltage, current, temperature, and load conditions of the battery module. Based on these real-time data, multi-dimensional feature parameters are extracted, including multiple performance characteristics of the battery module, such as load fluctuations, temperature changes, and power consumption of the battery. A comprehensive evaluation of the working status of the battery module is performed based on the extracted multi-dimensional feature parameters, including battery health status, temperature distribution uniformity, and heat dissipation efficiency evaluation. For example, the evaluation of the battery health status can be performed by analyzing the voltage and current changes of the battery module to determine the battery's charge and discharge efficiency and whether there is aging, and generate a battery health index. The evaluation of temperature distribution uniformity is performed by monitoring the temperature of each part of the battery module to check whether there is local overheating or excessive temperature difference, and generate a temperature distribution index.

[0072] Next, based on these evaluation results, a set of multivariate evaluation indicators is generated. These indicators represent the overall state of the battery module under the current operating environment, including battery health, temperature balance, and heat dissipation efficiency. Based on the priority of these evaluation indicators, correction weights are assigned to each indicator, and a fine-tuning function is constructed. This fine-tuning function fine-tunes the heat dissipation strategy based on the actual battery state. Using real-time battery state evaluation indicators for reinforcement learning, a multivariate correction vector is generated. This vector contains the adjustment values for each parameter of the optimal heat dissipation strategy, accurately identifying areas requiring enhanced heat dissipation and the direction in which the heat dissipation intensity should be adjusted.

[0073] Modifying the initial optimal cooling strategy based on a multi-dimensional correction vector ensures that cooling control can flexibly adapt to the real-time needs of the battery module. For example, if the target battery module has uneven temperature distribution or insufficient cooling efficiency, the cooling of high-temperature areas can be increased, or the cooling system's efficiency can be enhanced. Through this refined correction, the cooling strategy will better adapt to the operating conditions of the battery module in different working environments, further improving the cooling effect and avoiding battery performance degradation caused by uneven or excessive cooling. This intelligent optimization mechanism makes cooling control more flexible, maximizes the service life of the battery module, and improves the reliability of deep-sea exploration equipment.

[0074] In summary, the embodiments of the present application have at least the following technical effects:

[0075] This application collects the working environment monitoring information of the battery module of deep-sea exploration equipment in real time, matches the optimal dynamic heat dissipation mode based on this information, adapts to various working environment scenarios, divides the battery module into multiple independent heat dissipation modules, and configures each module with an independent heat dissipation unit. It collects battery working status data in real time, performs hotspot distribution analysis, generates module temperature gradients, and implements stepped layered heat dissipation control based on the modular heat dissipation architecture and the temperature gradient and optimal heat dissipation mode.

[0076] The technical effect of improving the heat dissipation efficiency and performance of battery modules in deep-sea environments has been achieved through a modular battery heat dissipation architecture and a stepped layered dynamic heat dissipation strategy.

[0077] Embodiment 2 is based on the same inventive concept as the adaptive heat dissipation method of the modular battery module in the above embodiment. Figure 2 As shown, the present application provides an adaptive heat dissipation system for modular battery modules. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0078] The working environment monitoring module 11 is used to collect working environment monitoring information of a target battery module in real time. The target battery module is applied to deep-sea exploration equipment.

[0079] The dynamic heat dissipation mode matching module 12 is used to match and obtain an optimal dynamic heat dissipation mode based on the working environment monitoring information, wherein the optimal dynamic heat dissipation mode corresponds to multiple battery working environment scenarios.

[0080] The battery heat dissipation architecture building module 13 is used to divide the target battery module into multiple independent heat dissipation modules, and build a modular battery heat dissipation architecture based on the multiple independent heat dissipation modules, wherein each heat dissipation module is configured with a corresponding independent heat dissipation unit.

[0081] The hotspot distribution analysis module 14 is used to collect the working status data of the target battery module in real time based on the independent heat dissipation unit, and perform hotspot distribution analysis according to the working status data to generate a module temperature gradient.

[0082] The stepped layered heat dissipation control module 15 is used to perform stepped layered heat dissipation control based on the modular battery heat dissipation architecture according to the module temperature gradient and the optimal dynamic heat dissipation mode.

[0083] Furthermore, the dynamic heat dissipation mode matching module 12 is further configured to perform the following steps:

[0084] According to the working area location of the deep-sea exploration equipment, the target deep-sea environmental characteristics are extracted; based on the target deep-sea environmental characteristics, clustering is performed to generate multiple dynamic heat dissipation modes, and a dynamic heat dissipation mode library is established based on this; according to the working environment monitoring information, the dynamic heat dissipation mode library is traversed to match the optimal dynamic heat dissipation mode.

[0085] Furthermore, the hotspot distribution analysis module 14 is further configured to perform the following steps:

[0086] Obtain the spatial arrangement information of the target battery module, perform spatial positioning, and generate a spatial coordinate code for each independent battery module; based on the working status data, obtain the operating temperature information of each independent battery module; based on the operating temperature information of each independent battery module, combine the spatial coordinate code to generate a spatial thermal distribution map; based on the spatial thermal distribution map, perform hot spot distribution analysis to generate the module temperature gradient.

[0087] Furthermore, the hotspot distribution analysis module 14 is further configured to perform the following steps:

[0088] Based on the spatial thermal distribution map, multiple temperature diffusion center points are extracted; for the multiple temperature diffusion center points, a neighborhood temperature diffusion trend analysis is performed, and multiple temperature control areas are divided according to the temperature diffusion trend; based on the multiple temperature control areas, a temperature change data sequence of each independent heat dissipation module is extracted in an associated manner; based on the temperature change data sequence, the temperature change direction of each area is determined, and based on the center point temperature of the multiple temperature diffusion center points, multiple temperature change vectors are generated; based on the multiple temperature change vectors, heat dissipation priorities are sorted to generate the module temperature gradient.

[0089] Furthermore, the stepped layered heat dissipation control module 15 is further configured to perform the following steps:

[0090] According to the module temperature gradient, the hierarchical heat dissipation priority is determined, and the hierarchical heat dissipation priority includes the heat dissipation priority identification of each independent battery module; based on the dynamic heat dissipation mode, the initial heat dissipation strategy is matched, and based on the hierarchical heat dissipation priority and hierarchical subdivision optimization, an optimal heat dissipation strategy is generated; according to the modular battery heat dissipation architecture, the stepped hierarchical heat dissipation control of the optimal heat dissipation strategy is executed.

[0091] Furthermore, the stepped layered heat dissipation control module 15 is further configured to perform the following steps:

[0092] Based on the dynamic heat dissipation mode, the basic heat dissipation strategy library is traversed to match the initial heat dissipation strategy, and the initial heat dissipation strategy includes the basic operating parameters of each independent heat dissipation unit; according to the hierarchical heat dissipation priority, the initial heat dissipation strategy is optimized in a first-order strategy to generate a first-order heat dissipation strategy; combined with the spatial arrangement information of the target battery module, heat dissipation interference analysis is performed to generate multiple field interference coefficients; based on the multiple field interference coefficients, the first-order heat dissipation strategy is optimized in a second-order strategy to generate the optimal heat dissipation strategy.

[0093] Furthermore, the stepped layered heat dissipation control module 15 is further configured to perform the following steps:

[0094] Receive working status data of a target battery module and extract multidimensional feature parameters based on the working status data; evaluate the working status of the battery module based on the multidimensional feature parameters, including evaluating the battery health status, temperature distribution uniformity, and heat dissipation efficiency, and generate a multivariate evaluation index; construct a fine-tuning function according to the multivariate evaluation index according to the index priority, and perform reinforcement learning through the fine-tuning function to generate a multivariate correction vector; and correct the optimal heat dissipation strategy based on the multivariate correction vector.

[0095] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0097] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. An adaptive heat dissipation method for a modular battery module, characterized in that: The method comprises: Real-time collection of working environment monitoring information of a target battery module, wherein the target battery module is used in deep-sea exploration equipment; Based on the working environment monitoring information, an optimal dynamic heat dissipation mode is matched and obtained, including: Extract the target deep-sea environmental characteristics based on the working area location of the deep-sea detection equipment; Clustering is performed based on the target deep-sea environment characteristics to generate multiple dynamic heat dissipation modes, and a dynamic heat dissipation mode library is established based on these modes; According to the working environment monitoring information, traverse the dynamic heat dissipation mode library to match the optimal dynamic heat dissipation mode; The optimal dynamic heat dissipation mode corresponds to a variety of battery working environment scenarios; Divide the target battery module into multiple independent heat dissipation modules, and build a modular battery heat dissipation architecture based on the multiple independent heat dissipation modules, wherein each heat dissipation module is configured with a corresponding independent heat dissipation unit; Based on the independent heat dissipation unit, the working status data of the target battery module is collected in real time, and the hot spot distribution is analyzed according to the working status data to generate the module temperature gradient; The performing hotspot distribution analysis based on the working status data to generate a module temperature gradient includes: Obtaining spatial arrangement information of the target battery module, performing spatial positioning, and generating a spatial coordinate code for each independent battery module; Based on the working status data, obtaining operating temperature information of each independent battery module; Generate a spatial thermal distribution map based on the operating temperature information of each independent battery module and the spatial coordinate code; Based on the spatial thermal distribution map, performing hotspot distribution analysis to generate the module temperature gradient; Based on the modular battery heat dissipation architecture, step-by-step hierarchical heat dissipation control is performed according to the module temperature gradient and the optimal dynamic heat dissipation mode, including: Determining a layered heat dissipation priority according to the module temperature gradient, wherein the layered heat dissipation priority includes a heat dissipation priority identifier of each independent battery module; Based on the dynamic heat dissipation mode, an initial heat dissipation strategy is matched, and based on the hierarchical heat dissipation priority and hierarchical subdivision optimization, an optimal heat dissipation strategy is generated; According to the modular battery heat dissipation architecture, the step-by-step hierarchical heat dissipation control of the optimal heat dissipation strategy is executed.

2. The adaptive heat dissipation method of a modular battery module according to claim 1, wherein: Based on the spatial thermal distribution map, hotspot distribution analysis is performed, including: Extracting a plurality of temperature diffusion center points based on the spatial thermal distribution map; Performing neighborhood temperature diffusion trend analysis on the multiple temperature diffusion center points, and dividing multiple temperature control areas according to the temperature diffusion trends; Taking the multiple temperature control areas as a reference, extracting the temperature change data sequence of each independent heat dissipation module in an associated manner; Determining the temperature change direction of each region according to the temperature change data sequence, and generating a plurality of temperature change vectors by combining the center point temperatures of the plurality of temperature diffusion center points; According to the multiple temperature change vectors, heat dissipation priorities are sorted to generate the module temperature gradient.

3. The adaptive heat dissipation method of a modular battery module according to claim 1, wherein: Based on the dynamic heat dissipation mode, an initial heat dissipation strategy is matched, and hierarchical subdivision optimization is performed based on the hierarchical heat dissipation priority, including: Based on the dynamic heat dissipation mode, traverse the basic heat dissipation strategy library to match the initial heat dissipation strategy, wherein the initial heat dissipation strategy includes the basic operating parameters of each independent heat dissipation unit; performing first-order strategy optimization on the initial heat dissipation strategy according to the hierarchical heat dissipation priority to generate a first-order heat dissipation strategy; Combined with the spatial arrangement information of the target battery module, heat dissipation interference analysis is performed to generate multiple field interference coefficients; Based on the interference coefficients of the multiple fields, the first-order heat dissipation strategy is optimized by a second-order strategy to generate the optimal heat dissipation strategy.

4. The adaptive heat dissipation method of a modular battery module according to claim 1, wherein: Before executing the step-by-step hierarchical heat dissipation control of the optimal heat dissipation strategy, the method further includes: receiving operating status data of a target battery module, and extracting multidimensional feature parameters based on the operating status data; Evaluate the working status of the battery module based on the multidimensional characteristic parameters, including evaluating the battery health status, temperature distribution uniformity, and heat dissipation efficiency, and generate multivariate evaluation indicators; Based on the multivariate evaluation indicators, a fine-tuning function is constructed according to the indicator priority, and reinforcement learning is performed through the fine-tuning function to generate a multivariate correction vector; The optimal heat dissipation strategy is corrected based on the multivariate correction vector.

5. The adaptive heat dissipation system of the modular battery module is characterized by: The system is used to execute the adaptive heat dissipation method of the modular battery module according to any one of claims 1 to 4, and the system includes: A working environment monitoring module, which is used to collect working environment monitoring information of a target battery module in real time, and the target battery module is used in deep-sea exploration equipment; a dynamic heat dissipation mode matching module, configured to match and obtain an optimal dynamic heat dissipation mode based on the working environment monitoring information, wherein the optimal dynamic heat dissipation mode corresponds to a plurality of battery working environment scenarios; A battery heat dissipation architecture building module, which is used to divide the target battery module into multiple independent heat dissipation modules and build a modular battery heat dissipation architecture based on the multiple independent heat dissipation modules, wherein each heat dissipation module is configured with a corresponding independent heat dissipation unit; a hotspot distribution analysis module, which is used to collect working status data of the target battery module in real time based on the independent heat dissipation unit, and perform hotspot distribution analysis based on the working status data to generate a module temperature gradient; A stepped layered heat dissipation control module is used to perform stepped layered heat dissipation control based on the modular battery heat dissipation architecture according to the module temperature gradient and the optimal dynamic heat dissipation mode.

Images