Self-adaptive heat dissipation method and system of modular battery module

Abstract

The invention discloses a self-adaptive heat dissipation method and system for a modular battery module, and relates to the technical field of intelligent control, and the method comprises the steps: collecting the working environment monitoring information of a target battery module of deep sea detection equipment in real time, and carrying out the matching to obtain an optimal dynamic heat dissipation mode; a target battery module is divided into a plurality of independent heat dissipation modules, a modular battery heat dissipation framework is built, working state data of the target battery module is collected in real time to perform hot spot distribution analysis, a module temperature gradient is generated, and stepped layered heat dissipation control is performed in combination with the modular battery heat dissipation framework and an optimal dynamic heat dissipation mode. According to the invention, the technical problems that the heat dissipation efficiency is low and the battery performance is reduced due to the fact that an existing battery module cannot effectively cope with high-pressure, low-temperature and corrosive conditions in a deep sea environment are solved, and the technical effects that the heat dissipation efficiency is low and the battery performance is reduced are achieved through a modular battery heat dissipation framework and a stepped layered dynamic heat dissipation strategy; and the heat dissipation efficiency and the performance of the battery module in the deep sea environment are improved.

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 a long time under high pressure, low temperature and seawater corrosion. 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 cooling support in deep-sea environments. In addition, deep-sea exploration equipment usually works in a changing environment, and the cooling requirements of the battery module vary with factors such as depth, pressure and temperature. Existing cooling systems cannot be adjusted intelligently, and often have problems such as low cooling efficiency, waste of resources and battery overheating. Summary of the invention

[0003] The present application provides an adaptive heat dissipation method and system for a modular battery module, 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 the deep sea environment, resulting in low heat dissipation efficiency and reduced battery performance.

[0004] The first aspect of the present application provides an adaptive heat dissipation method for modular battery modules, 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 a plurality of independent heat dissipation modules, and building a modular battery heat dissipation architecture based on the plurality of 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 hot spot 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 hierarchical heat dissipation control according to the module temperature gradient and the optimal dynamic heat dissipation mode.

[0005] According to a second aspect of the present application, an adaptive heat dissipation system for a modular battery module is provided, the system comprising: a working environment monitoring module, the working environment monitoring module 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; a dynamic heat dissipation mode matching module, the dynamic heat dissipation mode matching module 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 a variety of battery working environment scenarios; a battery heat dissipation architecture building module, the battery heat dissipation architecture building module is used to divide the target battery module into a plurality of independent heat dissipation modules, and build a modular battery heat dissipation architecture based on the plurality of independent heat dissipation modules, wherein each heat dissipation module is configured with a corresponding independent heat dissipation unit; a hot spot distribution analysis module, the hot spot distribution analysis module is used to collect working status data of the target battery module in real time based on the independent heat dissipation unit, and perform hot spot distribution analysis according to the working status data to generate a module temperature gradient; a stepped layered heat dissipation control module, the stepped layered heat dissipation control module is 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: 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 battery modules of deep-sea exploration equipment, the optimal dynamic heat dissipation mode is matched, and the modules are divided 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, and a temperature gradient is generated. A stepped layered heat dissipation control is implemented based on a modular architecture. 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 is solved. The technical effect of improving the heat dissipation efficiency and performance of the battery modules in the deep-sea environment is achieved through a modular battery heat dissipation architecture and a stepped layered dynamic heat dissipation strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] 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.

[0008] Figure 1 A schematic diagram of a flow chart of an adaptive heat dissipation method for a modular battery module provided in an embodiment of the present application; Figure 2A schematic diagram of the structure of an adaptive heat dissipation system for a modular battery module provided in an embodiment of the present application.

[0009] 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 hierarchical heat dissipation control module 15 . DETAILED DESCRIPTION

[0010] The present application provides an adaptive heat dissipation method and system for a modular battery module, 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 the deep sea environment, resulting in low heat dissipation efficiency and reduced battery performance.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] 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 that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides an adaptive heat dissipation method for a modular battery module, the method comprising: P10: Real-time collection of working environment monitoring information of a target battery module, wherein the target battery module is applied to deep-sea exploration equipment.

[0014] Specifically, it is first necessary to collect the working environment monitoring information of the target battery module in real time. 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 (pressures that can reach 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.

[0015] To this end, it is necessary to install multiple high-precision environmental monitoring sensors around and inside the target battery module. These sensors may include temperature sensors, pressure sensors, humidity sensors, current and voltage sensors, etc. These sensors can provide real-time feedback on environmental changes. For example, the temperature sensor can accurately monitor the temperature difference between the battery module and the surrounding environment, and the pressure sensor measures the impact of water pressure changes in the deep sea on the battery.

[0016] This multi-dimensional and multi-level data collection ensures that the complete working status information of the battery module under various environmental conditions is obtained, which in turn provides data basis for subsequent intelligent heat dissipation control and ensures that the battery module always maintains good performance during deep-sea exploration. This process is crucial for the real-time adjustment and emergency response of the system, and can effectively avoid equipment failure or performance degradation due to environmental changes.

[0017] 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 a variety of battery working environment scenarios.

[0018] Furthermore, step P20 of the embodiment of the present application further includes: P21: Extract the target deep-sea environmental characteristics according to the working area location of the deep-sea detection equipment; P22: Perform clustering based on the target deep-sea environmental characteristics, generate multiple dynamic heat dissipation modes, and establish a dynamic heat dissipation mode library based on this; P23: According to the working environment monitoring information, traverse the dynamic heat dissipation mode library to match the optimal dynamic heat dissipation mode.

[0019] It should be understood that by processing the working environment monitoring information, the optimal dynamic heat dissipation mode is intelligently matched according to the different working environment scenarios of the battery module to ensure that the battery can maintain good heat dissipation in different 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 working mode of the heat dissipation system according to the changing factors in the deep-sea environment (such as temperature, pressure, etc.) and the working state of the battery module (such as load, temperature, power, etc.), so as to achieve the most ideal heat dissipation effect. The selection of this mode is based on factors including ambient temperature, working depth, seawater pressure, etc., and avoids overheating or overcooling by accurately matching the corresponding heat dissipation method.

[0020] Specifically, firstly, based on the specific deep-sea location of the equipment, relevant deep-sea environmental characteristics are extracted. Deep-sea detection equipment usually works in different sea areas, and the environmental characteristics (such as seawater temperature, salinity, pressure, etc.) in which it is located will vary greatly. By analyzing the working area location of the target equipment (such as water depth, geographical location, etc.), the environmental data characteristics related to the location are extracted. For example, in shallow waters, the temperature is higher and the pressure is lower, while in deep waters, the temperature is lower and the pressure is higher. The extraction of this data will help to match the optimal heat dissipation mode later.

[0021] 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, you can choose an enhanced heat dissipation mode in a high-temperature environment, and in a low-temperature environment, you can choose a lower-level heat dissipation mode. 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 use algorithms such as K-means clustering and hierarchical clustering to divide environmental characteristic data into several different categories based on their similarity, providing a variety of options for subsequent matching of heat dissipation modes.

[0022] Next, based on the real-time collected working environment monitoring information, the established dynamic heat dissipation mode library is traversed, and the heat dissipation mode that best suits the current environmental conditions is selected based on the current working status of the battery module (such as load, battery temperature, ambient temperature, etc.) and environmental data. By matching with the preset modes in the mode library, it can quickly respond to environmental changes and adjust the working mode of the heat dissipation system in a timely manner. The matching process can be implemented through algorithms, such as similarity matching or nearest neighbor search, to screen various heat dissipation strategies in the mode library according to real-time monitored environmental variables and select the most suitable heat dissipation mode. This process enables deep-sea exploration equipment to flexibly respond to various complex deep-sea environments and ensure the continuous and efficient operation of the battery module.

[0023] Through the above steps, the entire system can intelligently select the optimal dynamic heat dissipation mode according to 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.

[0024] 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.

[0025] Optionally, in order to meet the heat dissipation requirements of the target battery module in the 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 works 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.

[0026] Specifically, the target battery module is reasonably divided into multiple independent heat dissipation modules according to the structural characteristics of the battery and the distribution of heat generation. The purpose of this division is to better cope with the different temperature gradients generated in different areas of the battery module and avoid the limitations brought about by over-reliance on a single heat dissipation method. Different parts of the battery module may generate different amounts of heat due to different loads when working. The modular design can provide more precise heat dissipation treatment for hot spots.

[0027] 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 be adjusted autonomously according to the working area in which it is located to ensure that the temperature control in the area is always in the best state. For example, in areas where the battery module has a high load and a large amount of heat, the heat dissipation module can be equipped with more efficient heat dissipation equipment, such as a high thermal conductivity heat sink or a liquid cooling pipe. In areas with low loads and more stable temperatures, simpler heat dissipation methods can be used.

[0028] Through this modular design, the battery module can flexibly respond to the heat dissipation needs of deep-sea exploration equipment in different working environments and working conditions. In deep-sea environments, due to the drastic changes in pressure and temperature, the battery module may need to frequently adjust its heat dissipation method. The modular design makes the entire system more adaptable and flexible, and can adjust the heat dissipation method of each heat dissipation module according to the real-time working environment conditions, thereby ensuring the overall stable operation of the equipment.

[0029] 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, which facilitates subsequent maintenance 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 a strong guarantee for the stable operation of deep-sea exploration equipment in harsh environments.

[0030] P40: 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 a module temperature gradient.

[0031] Furthermore, step P40 of the embodiment of the present application also includes: 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: According to 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 hot spot distribution analysis to generate the module temperature gradient.

[0032] 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 these data to generate the temperature gradient of the module, so as to provide an accurate basis for subsequent heat dissipation control.

[0033] 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 the equipment in a specific way. In order 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. And based on this information, a spatial coordinate code is generated for each independent battery module. The code is a unique identifier that indicates the exact 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 the temperature data can be accurately located to each battery module.

[0034] Next, based on the collected working status data, the operating temperature information of each independent battery module is obtained. In this process, the working status of the battery module can be monitored in real time using temperature sensors and working status monitoring equipment. The working status data of the battery module is collected through precise sensors, mainly including data such as the voltage, current, load and ambient temperature of the battery cell. These data will help the system predict the temperature rise of each battery cell. Especially when the load is high or during charging, the battery temperature usually rises. By collecting these temperature data, the temperature change trend of the battery module can be fully understood. 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, so that more accurate heat dissipation measures can be formulated based on this temperature information.

[0035] Next, the operating temperature information of each independent battery module is used in combination with the spatial coordinate encoding to generate a spatial thermal distribution map. This process first matches the operating temperature of the battery module with the spatial coordinate encoding, 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 thermal 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.

[0036] Finally, based on the generated spatial thermal distribution map, the system performs hotspot distribution analysis to generate the 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 need to be focused on and take 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 positions 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.

[0037] Furthermore, step P44 of the embodiment of the present application also includes: 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.

[0038] 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.

[0039] First, based on the spatial thermal distribution map, multiple temperature diffusion center points are extracted. In the generated spatial thermal distribution map, the temperature in some areas is higher. These areas are usually where heat is concentrated and may cause overheating problems. Therefore, the temperature peak or hot spot area in the spatial thermal distribution map can be detected by an algorithm, and multiple temperature diffusion center points can be extracted from the thermal map. These center points represent the core area of ​​heat propagation and are the starting point of heat diffusion. The temperature value of each temperature diffusion center point is usually high, and the surrounding area will be affected by the diffusion of heat.

[0040] Next, for 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. By analyzing the temperature change trend of the areas around multiple temperature diffusion center points, the trend of heat diffusion in the surrounding areas can be determined. Exemplarily, a data processing algorithm can be used to analyze the neighborhood temperature change of each temperature diffusion center point to predict the heat diffusion trend. According to these temperature diffusion trends, the heat diffusion range is divided into multiple temperature control areas, and the area where the heat diffusion is more concentrated will be divided into an independent temperature control area, while the area with a relatively stable temperature change will form another area.

[0041] On this basis, taking the multiple temperature control areas as the benchmark, the temperature change data sequence of each independent heat dissipation module is associated and extracted, that is, the temperature change data sequence within a period of time. By associating these data, the system can understand in detail the temperature change trend of each heat dissipation module within the temperature control area.

[0042] Next, based on the temperature change data sequence, determine the direction of temperature change in each area, that is, whether the temperature is rising or falling. This process is achieved by calculating parameters such as the rate and direction of temperature change. On this basis, multiple temperature change vectors are generated in combination with the center point temperature of each temperature diffusion center point. The temperature change vector represents the direction and intensity of temperature change, and can provide a quantitative basis for subsequent heat dissipation decisions. Each vector indicates the trend and speed of temperature change in a certain area and is a component of the temperature gradient.

[0043] Finally, after the temperature change vector is generated, the priority of heat dissipation is determined through a priority sorting algorithm based on the temperature change rate and direction of each area. Areas with faster temperature changes or in the direction of rising temperatures will be given a higher heat dissipation priority so that heat dissipation measures can be taken in time to prevent overheating. Areas with slower temperature changes or in the direction of cooling will have a lower heat dissipation priority. This heat dissipation priority sorting ensures that during the heat dissipation process of the battery module, the areas that need heat dissipation the most can be processed first, maximizing the heat dissipation effect and preventing excessive temperature from affecting battery performance.

[0044] Through this series of steps, not only can the temperature change trend of each temperature control area be accurately identified, but also a scientific and reasonable heat dissipation strategy can be generated according to the actual temperature control requirements, and the heat dissipation requirements of each area can be responded to in a timely and effective manner through heat dissipation priority sorting. The module temperature gradient finally generated will provide a clear guidance basis for the heat dissipation control system of the battery module, ensuring that the equipment can continue to work stably in the complex deep-sea exploration environment.

[0045] P50: 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.

[0046] Furthermore, step P50 of the embodiment of the present application also includes: P51: Determine the hierarchical heat dissipation priority according to 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 hierarchical subdivision optimization; P53: According to the modular battery heat dissipation architecture, execute the stepped hierarchical heat dissipation control of the optimal heat dissipation strategy.

[0047] Specifically, based on the modular battery cooling architecture, step-by-step hierarchical 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, so as to achieve efficient cooling effects and maintain long-term stable operation of the equipment.

[0048] First, based on the module temperature gradient, extract the hierarchical heat dissipation priority of each independent heat dissipation module, that is, by analyzing the module temperature gradient, determine the heat dissipation level of each independent heat dissipation module, and perform priority calibration to calibrate the heat dissipation priority mark for each independent battery module. In this way, the system can ensure that during the heat dissipation process of the battery module, it can give priority to areas with higher temperatures or higher priorities to prevent battery performance degradation or damage due to overheating.

[0049] Next, based on the dynamic heat dissipation mode, the basic heat dissipation strategy library is traversed to match the initial heat dissipation strategy. The basic heat dissipation strategy library corresponds to the dynamic heat dissipation mode library and contains benchmark heat dissipation strategies for various battery working environment scenarios, which can be constructed based on empirical data training. The initial heat dissipation strategy can perform basic heat dissipation configuration for each independent battery module, such as determining the intensity and range of heat dissipation.

[0050] Subsequently, the initial heat dissipation strategy is subdivided and optimized according to the determined hierarchical heat dissipation priority. For example, the 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 more advanced 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 accurately controlled, and resources can be effectively utilized to avoid unnecessary energy waste due to excessive heat dissipation.

[0051] Finally, according to 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 priorities, 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 priorities, the cooling measures may be milder, and temperature control may be maintained only through natural convection or lower-power cooling units.

[0052] Step-by-step control not only ensures that the equipment achieves differentiated heat dissipation effects in different temperature control areas, but also avoids unnecessary energy consumption and reduces equipment operating costs. Each heat dissipation module performs heat dissipation control according to its own priority and needs, ensuring that the temperature of the entire battery module is accurately adjusted.

[0053] Furthermore, step P52 of the embodiment of the present application also includes: 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.

[0054] 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 the actual working environment, so as to achieve the best heat dissipation effect.

[0055] First, based on the dynamic heat dissipation mode, traverse the basic heat dissipation strategy library 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 suitable 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 specific working scenarios 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.

[0056] Then, according to the hierarchical heat dissipation priority, the initial heat dissipation strategy is optimized in the first order. 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, to ensure 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.

[0057] Next, combined with the spatial arrangement information of the target battery module, 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 effects of heat transfer 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.

[0058] Finally, based on the multiple domain interference coefficients, the first-order heat dissipation strategy is optimized by 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 through the domain interference coefficient to reduce the interference effect.

[0059] 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.

[0060] Furthermore, before executing the step-by-step 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: 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: According to 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.

[0061] 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.

[0062] First, receive the real-time working status data of the target battery module, which includes key parameters such as the voltage, current, temperature, and load conditions of the battery module. Based on these real-time data, extract multidimensional feature parameters, including multiple performance characteristics of the battery module, such as battery load fluctuations, temperature changes, and power consumption. A comprehensive evaluation of the working status of the battery module is performed based on the extracted multidimensional feature parameters, including battery health status, temperature distribution uniformity, and heat dissipation efficiency evaluation. Exemplarily, 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 charging and discharging efficiency and whether there is aging, and generate a battery health index. The evaluation of the uniformity of temperature distribution monitors the temperature of each part of the battery module to check whether there is local overheating or excessive temperature difference, and generates a temperature distribution index.

[0063] Next, based on the above evaluation results, a set of multivariate evaluation indicators are generated. These indicators represent the overall status of the battery module under the current working environment, including battery health, temperature balance, and heat dissipation efficiency. According to the priority of these evaluation indicators, correction weights are configured for each evaluation indicator, and a fine-tuning function is constructed. The fine-tuning function can fine-tune the heat dissipation strategy according to the actual state of the battery. By using real-time battery status evaluation indicators for reinforcement learning, a multivariate correction vector is generated. This vector contains the adjustment values ​​for various parameters of the optimal heat dissipation strategy, which can accurately point out the areas where heat dissipation needs to be strengthened, as well as the direction of adjusting the heat dissipation intensity.

[0064] Correcting the initial optimal heat dissipation strategy based on the multivariate correction vector ensures that the heat dissipation control can flexibly adapt to the real-time needs of the battery module. For example, if the temperature distribution of the target battery module is uneven or the heat dissipation efficiency is insufficient, the heat dissipation in the high-temperature area can be increased, or the efficiency of the cooling system can be enhanced. Through this refined correction, the heat dissipation strategy will better adapt to the operating conditions of the battery module under different working environments, further improve the heat dissipation effect and avoid battery performance degradation caused by uneven heat dissipation or excessive heat dissipation. This intelligent optimization mechanism makes heat dissipation control more flexible, maximizes the service life of the battery module and improves the reliability of deep-sea exploration equipment.

[0065] In summary, the embodiments of the present application have at least the following technical effects: 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 the 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. The battery working status data is collected in real time, and the hot spot distribution is analyzed to generate the module temperature gradient. Based on the modular heat dissipation architecture, a stepped hierarchical heat dissipation control is implemented according to the temperature gradient and the optimal heat dissipation mode.

[0066] 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.

[0067] 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 a modular battery module, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: The working environment monitoring module 11 is used to collect working environment monitoring information of a target battery module in real time, and the target battery module is applied to deep-sea detection equipment.

[0068] 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 a variety of battery working environment scenarios.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] Furthermore, the dynamic heat dissipation mode matching module 12 is also used to perform the following steps: According to the working area location of the deep-sea detection 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.

[0073] Furthermore, the hotspot distribution analysis module 14 is also used to perform the following steps: Acquire 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, acquire the 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 in combination with the spatial coordinate code; perform hot spot distribution analysis based on the spatial thermal distribution map to generate the module temperature gradient.

[0074] Furthermore, the hotspot distribution analysis module 14 is also used to perform the following steps: 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, the 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 multiple temperature change vectors are generated in combination with the center point temperatures of the multiple temperature diffusion center points; based on the multiple temperature change vectors, heat dissipation priorities are sorted to generate the module temperature gradient.

[0075] Furthermore, the stepped layered heat dissipation control module 15 is also used to perform the following steps: 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.

[0076] Furthermore, the stepped layered heat dissipation control module 15 is also used to perform the following steps: 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, a 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.

[0077] Furthermore, the stepped layered heat dissipation control module 15 is also used to perform the following steps: 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 multivariate evaluation indicators; construct a fine-tuning function according to the multivariate evaluation indicators and the indicator 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.

[0078] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0080] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

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 applied to deep-sea exploration equipment; Based on the working environment monitoring information, an optimal dynamic heat dissipation mode is matched and obtained, wherein the optimal dynamic heat dissipation mode corresponds to a plurality of battery working environment scenarios; Divide the target battery module into a plurality of independent heat dissipation modules, and build a modular battery heat dissipation architecture based on the plurality of 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; 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.

2. The adaptive heat dissipation method of a modular battery module according to claim 1, characterized in that: 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 according to 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, the dynamic heat dissipation mode library is traversed to match the optimal dynamic heat dissipation mode.

3. The adaptive heat dissipation method of a modular battery module according to claim 1, characterized in that: Performing hotspot distribution analysis according to the working status data to generate a module temperature gradient includes: Acquire 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, 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 in combination with the spatial coordinate coding; Based on the spatial thermal distribution diagram, hot spot distribution analysis is performed to generate the module temperature gradient.

4. The adaptive heat dissipation method of a modular battery module according to claim 3, characterized in that: Based on the spatial thermal distribution map, hotspot distribution analysis is performed, including: Based on the spatial thermal distribution map, extracting multiple temperature diffusion center points; For the multiple temperature diffusion center points, a neighborhood temperature diffusion trend analysis is performed, and a plurality of temperature control areas are divided according to the temperature diffusion trend; Taking the multiple temperature control areas as a reference, extracting the temperature change data sequence of each independent heat dissipation module in association; Determine the temperature change direction of each region according to the temperature change data sequence, and generate multiple temperature change vectors in combination with the center point temperatures of the multiple temperature diffusion center points; According to the multiple temperature change vectors, heat dissipation priorities are sorted to generate the module temperature gradient.

5. The adaptive heat dissipation method of a modular battery module according to claim 4, characterized in that: Performing step-by-step hierarchical heat dissipation control according to the module temperature gradient and dynamic heat dissipation mode includes: 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 stepped hierarchical heat dissipation control of the optimal heat dissipation strategy is executed.

6. The adaptive heat dissipation method of a modular battery module according to claim 5, characterized in that: 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 cooling mode, traverse the basic cooling strategy library to match the initial cooling strategy, wherein the initial cooling strategy includes the basic operating parameters of each independent cooling unit; According to the hierarchical heat dissipation priority, the initial heat dissipation strategy is optimized by 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 interference coefficients in multiple fields; 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.

7. The adaptive heat dissipation method of a modular battery module according to claim 5, characterized in that: Before executing the step-by-step hierarchical heat dissipation control of the optimal heat dissipation strategy, the method further includes: Receiving working status data of a target battery module, and extracting multi-dimensional feature parameters based on the working status data; Based on the multi-dimensional characteristic parameters, the working state of the battery module is evaluated, including the battery health state, temperature distribution uniformity and heat dissipation efficiency evaluation, and a multi-evaluation index is generated; According to 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; Based on the multivariate correction vector, the optimal heat dissipation strategy is corrected.

8. The adaptive cooling system of the modular battery module is characterized by: The system comprises: 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 applied to deep-sea exploration equipment; A dynamic heat dissipation mode matching module, the dynamic heat dissipation mode matching module 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 a variety of battery working environment scenarios; A battery heat dissipation architecture building module, the battery heat dissipation architecture building module 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 according to 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.

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