Method for controlling an energy storage liquid cooling system by artificial intelligence

Abstract

The application discloses an artificial intelligence control energy storage liquid cooling system method, and relates to the technical field of energy storage system cooling; comprising: step 1: improving the liquid cooling energy storage system, step 2: collecting the thermal energy data of the liquid cooling energy storage system through the sensor, step 3: analyzing and processing the thermal energy data to obtain the analysis result, obtaining the operation state, heat distribution and heat dissipation effect data of the liquid cooling energy storage system according to the analysis result, step 4: based on the operation state, heat distribution and heat dissipation effect data of the liquid cooling energy storage system, constructing and training an artificial intelligence prediction model, using the artificial intelligence prediction model to predict the future thermal state development trend by learning the historical data and real-time data, and making corresponding decisions according to the current thermal state, step 5: according to the thermal state development trend predicted by the artificial intelligence prediction model and the provided decisions, formulating and executing corresponding thermal energy control strategies: according to the predicted thermal state development trend, adjusting the operation parameters of the liquid cooling energy storage system, controlling each component of the liquid cooling energy storage system to real-time regulate and control the liquid cooling energy storage system.

Description

Technical Field

[0001] This invention discloses a method relating to the field of energy storage system cooling technology, specifically a method for controlling an energy storage liquid cooling system using artificial intelligence. Background Technology

[0002] Temperature is one of the most significant factors affecting the capacity, power, and safety of lithium batteries. Compared to power battery systems, energy storage systems contain more batteries and operate under more complex conditions, which can easily lead to various problems, including uneven temperature distribution and excessive temperature differences between cells. This can affect many aspects of the system's performance, including charging and discharging, and potentially cause thermal runaway.

[0003] Currently, common energy storage heat dissipation technologies are mainly divided into four types: air cooling, liquid cooling, heat pipes, and phase change cooling. Liquid cooling technology introduces coolant into the energy storage device through conduits or cold plates, utilizing the thermal conductivity of liquids for heat dissipation. Compared to air cooling technology, liquid cooling technology has certain advantages in heat dissipation efficiency and can achieve refined thermal management. Heat pipe technology is suitable for energy storage devices requiring high thermal conductivity and small size, but it has limitations in high-power applications. For example, in energy storage systems with more batteries, the main heat-generating parts of the battery cell are at the positive and negative terminals. However, due to the wiring of the terminals and the complexity and cost of the design, the liquid cooling plate in current liquid-cooled energy storage systems is usually located at the bottom of the battery, resulting in relatively poor heat dissipation. Furthermore, current thermal management control strategies are not precise enough and require further improvement and optimization. Summary of the Invention

[0004] This invention addresses the problems of existing technologies by providing an artificial intelligence-controlled liquid cooling system for energy storage. This method addresses the thermal management of liquid cooling energy storage systems, maintaining stable temperatures and improving heat dissipation efficiency, while meeting diverse application requirements and accurately monitoring and controlling battery temperature.

[0005] The specific solution proposed in this invention is as follows:

[0006] This invention provides a method for controlling an energy storage liquid cooling system using artificial intelligence, comprising:

[0007] Step 1: Improve the liquid-cooled energy storage system: Utilize an alumina ceramic liquid cooling plate, placing it directly on top of the battery chips. Deploy a capillary network and thermally conductive material inside the liquid cooling plate. The capillary network distributes the coolant and increases the surface area of ​​the liquid cooling plate, improving heat dissipation efficiency. The thermally conductive material transfers heat from the battery chips to the capillary network and other parts of the liquid cooling plate.

[0008] Step 2: Collect thermal energy data from the liquid-cooled energy storage system using sensors. This thermal energy data includes battery temperature, liquid cooling plate temperature, and liquid cooling flow rate data. Process the thermal energy data.

[0009] Step 3: Analyze the processed thermal energy data to obtain analysis results. Based on the analysis results, obtain data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system.

[0010] Step 4: Based on data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system, construct and train an artificial intelligence prediction model. Utilize this model to predict future thermal trends by learning from historical and real-time data, and make corresponding decisions based on the current thermal state.

[0011] Step 5: Based on the thermal development trend predicted by the artificial intelligence prediction model and the decisions provided, formulate and implement corresponding thermal energy control strategies: adjust the operating parameters of the liquid-cooled energy storage system according to the predicted thermal development trend, and control the real-time regulation of the liquid-cooled energy storage system by each component of the liquid-cooled energy storage system.

[0012] Furthermore, step 1 of the method for controlling an artificial intelligence-controlled liquid cooling energy storage system further includes: configuring a liquid cooling unit, liquid cooling pipelines, high and low voltage wiring harnesses, and coolant for the liquid cooling energy storage system, wherein a heater is configured for the liquid cooling unit, the coolant in the liquid cooling unit is cooled by a compressor, and the cooled coolant is heated or its temperature is regulated by the heater. Temperature sensors and valves are deployed according to the liquid cooling pipelines, and the type of coolant is selected.

[0013] Furthermore, in step 5 of the method for controlling an artificial intelligence-controlled liquid cooling energy storage system, the real-time regulation of the liquid cooling energy storage system by controlling the various components of the liquid cooling energy storage system includes:

[0014] The workflow of controlling a liquid-cooled energy storage system includes:

[0015] Coolant is pumped or circulated to the liquid cooling plate through liquid cooling pipes, so that the capillary network on the liquid cooling plate can evenly distribute the coolant to the surface of the liquid cooling plate.

[0016] The heat from the battery chip is transferred to the liquid cooling plate by the thermally conductive material on the plate, while the coolant on the surface of the plate exchanges heat with the top of the battery chip, absorbing the heat.

[0017] The heated coolant is pumped to the liquid chiller unit through coolant piping. In the liquid chiller unit, the coolant is cooled by the compressor, and then heated or regulated by the heater.

[0018] The heat in the coolant is dissipated to the surrounding environment through the radiator in the liquid cooling unit;

[0019] After being cooled, the coolant is pumped back to the liquid cooling plate, and the cycle repeats to exchange heat.

[0020] Furthermore, the method for controlling an artificial intelligence-controlled liquid cooling energy storage system also includes step 6: remotely monitoring the liquid cooling energy storage system via the cloud and managing the liquid cooling energy storage system through an artificial intelligence prediction model.

[0021] This invention provides a device for controlling an energy storage liquid cooling system using artificial intelligence, comprising a configuration module, a data processing module, an analysis module, an artificial intelligence model module, and a control module.

[0022] Improved configuration module for liquid-cooled energy storage system: Utilizing an alumina ceramic liquid cooling plate, the liquid cooling plate of the energy storage system is directly attached to the top of the battery chips in a top-mounted manner. A capillary network and thermally conductive materials are deployed inside the liquid cooling plate. The capillary network distributes the coolant and increases the surface area of ​​the liquid cooling plate to improve heat dissipation efficiency. The thermally conductive materials transfer heat from the battery chips to the capillary network and other parts of the liquid cooling plate.

[0023] The data processing module collects thermal energy data from the liquid-cooled energy storage system via sensors. This thermal energy data includes battery temperature, liquid cooling plate temperature, and liquid cooling flow rate. The module then processes this thermal energy data.

[0024] The analysis module analyzes and processes the thermal energy data to obtain analysis results. Based on these results, data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system are obtained.

[0025] The artificial intelligence model module builds and trains an AI prediction model based on data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system. This model learns from historical and real-time data to predict future thermal trends and makes corresponding decisions based on the current thermal state.

[0026] Based on the thermal development trend predicted by the artificial intelligence prediction model and the decisions provided, the control module formulates and executes corresponding thermal energy control strategies: according to the predicted thermal development trend, it adjusts the operating parameters of the liquid-cooled energy storage system and controls the real-time regulation of the liquid-cooled energy storage system by each component of the liquid-cooled energy storage system.

[0027] Furthermore, the configuration module in the aforementioned artificial intelligence-controlled liquid cooling energy storage system also includes a liquid cooling unit, liquid cooling pipelines, high and low voltage wiring harnesses, and coolant for the liquid cooling energy storage system. A heater is configured for the liquid cooling unit. In the liquid cooling unit, the coolant is cooled by a compressor, and the cooled coolant is heated or its temperature is regulated by the heater. Temperature sensors and valves are deployed according to the liquid cooling pipelines, and the type of coolant is selected.

[0028] Furthermore, in the aforementioned artificial intelligence-controlled liquid cooling energy storage system device, the control module controls the various components of the liquid cooling energy storage system to perform real-time regulation of the liquid cooling energy storage system, including: controlling the workflow of the liquid cooling energy storage system, including:

[0029] Coolant is pumped or circulated to the liquid cooling plate through liquid cooling pipes, so that the capillary network on the liquid cooling plate can evenly distribute the coolant to the surface of the liquid cooling plate.

[0030] The heat from the battery chip is transferred to the liquid cooling plate by the thermally conductive material on the plate, while the coolant on the surface of the plate exchanges heat with the top of the battery chip, absorbing the heat.

[0031] The heated coolant is pumped to the liquid chiller unit through coolant piping. In the liquid chiller unit, the coolant is cooled by the compressor, and then heated or regulated by the heater.

[0032] The heat in the coolant is dissipated to the surrounding environment through the radiator in the liquid cooling unit;

[0033] After being cooled, the coolant is pumped back to the liquid cooling plate, and the cycle repeats to exchange heat.

[0034] Furthermore, the device for controlling an artificial intelligence-controlled liquid cooling energy storage system also includes a cloud module. The cloud module remotely monitors the liquid cooling energy storage system via the cloud and manages the liquid cooling energy storage system through an artificial intelligence prediction model.

[0035] The advantages of this invention are:

[0036] This invention provides a method for controlling an energy storage liquid cooling system using artificial intelligence. It employs an alumina ceramic liquid cooling plate, positioned at the top and directly contacting the positive and negative electrodes of the main heat-generating parts of the battery cell. Compared to a water-cooled plate placed at the bottom of the battery cell, this method offers significantly higher cooling efficiency, primarily in the following aspects:

[0037] 1. Increased contact area: The top-mounted design allows the water-cooling plate to directly contact the main heat-generating parts of the battery cell's positive and negative terminals, resulting in a larger contact area compared to the traditional bottom-mounted design. This allows for more effective heat transfer from the battery cell to the water-cooling plate, increasing heat transfer and dissipation efficiency.

[0038] 2. Shortened heat transfer path: The top-mounted design shortens the heat transfer path. Heat can be transferred only through the thermally conductive material between the electrodes and the liquid cooling plate inside the cell, without needing to pass through the cell's packaging structure. This reduces thermal resistance and improves heat transfer efficiency.

[0039] 3. Superior thermal conductivity: Alumina ceramic materials have high thermal conductivity, which can conduct and disperse heat more effectively than traditional metal water-cooling plates. Using alumina ceramic water-cooling plates can improve heat dissipation, more effectively reduce cell temperature, and improve the system's thermal management capabilities.

[0040] By using artificial intelligence models to predict thermal trends based on collected data from the battery cells, refined thermal management can be achieved. Compared to adjusting solely based on cell temperature, intelligent thermal management enables more precise and intelligent thermal management strategies in the following aspects:

[0041] 1. Dynamic Adjustment of Liquid Cooling Unit Operating Status: Intelligent thermal management can adjust the operating status of the liquid cooling unit in real time based on predicted cell conditions. Through analysis and prediction, the system can anticipate the temperature rise trend of the cells and the dynamic changes in heat generation and dissipation. Based on this information, the system can dynamically adjust the operating parameters of the liquid cooling unit, such as the coolant flow rate and temperature, to achieve more precise thermal management and control.

[0042] 2. Energy Saving and Reduced Power Consumption: Intelligent thermal management can rationally optimize the operation strategy of the liquid cooling unit based on model and prediction results to reduce power consumption. By finely controlling the operating status of the liquid cooling unit, the cell temperature is maintained within a suitable range, avoiding overcooling or overheating. This avoids unnecessary energy consumption, reduces system power consumption, and improves energy utilization efficiency.

[0043] 3. Fault Warning and Safety Protection: Intelligent thermal management can identify abnormal conditions in battery cells in a timely manner through models and predictions, and provide fault warnings and safety protection. The system can monitor the temperature, pressure, and other key indicators of the battery cells, compare real-time data with predicted results, and take immediate corresponding measures, such as alarms, shutdowns, or adjustments to control strategies, if an anomaly is detected, to ensure the safety and reliability of the system.

[0044] 4. Statistical Analysis and Optimization Strategies: Intelligent thermal management can utilize extensive cell data and historical records for statistical analysis and optimization strategy development. The system can learn and identify thermal characteristics and energy consumption under different operating conditions, thereby optimizing thermal management strategies and improving system performance and efficiency. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of a liquid-cooled battery pack.

[0047] Figure 2 This is a schematic diagram of module interaction in the device of the present invention.

[0048] Attached reference numerals: 01 Top cover; 02 Alumina ceramic liquid cooling plate; 03 Negative electrode outlet; 04 Battery module; 05 Positive electrode inlet; 06 Lower casing plate. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0050] This invention provides a method for controlling an energy storage liquid cooling system using artificial intelligence, comprising:

[0051] Step 1: Improve the liquid-cooled energy storage system: Utilize an alumina ceramic liquid cooling plate, placing it directly on top of the battery chips. Deploy a capillary network and thermally conductive material inside the liquid cooling plate. The capillary network distributes the coolant and increases the surface area of ​​the liquid cooling plate, improving heat dissipation efficiency. The thermally conductive material transfers heat from the battery chips to the capillary network and other parts of the liquid cooling plate.

[0052] Step 2: Collect thermal energy data from the liquid-cooled energy storage system using sensors. This thermal energy data includes battery temperature, liquid cooling plate temperature, and liquid cooling flow rate data. Process the thermal energy data.

[0053] Step 3: Analyze the processed thermal energy data to obtain analysis results. Based on the analysis results, obtain data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system.

[0054] Step 4: Based on data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system, construct and train an artificial intelligence prediction model. Utilize this model to predict future thermal trends by learning from historical and real-time data, and make corresponding decisions based on the current thermal state.

[0055] Step 5: Based on the thermal development trend predicted by the artificial intelligence prediction model and the decisions provided, formulate and implement corresponding thermal energy control strategies: adjust the operating parameters of the liquid-cooled energy storage system according to the predicted thermal development trend, and control the real-time regulation of the liquid-cooled energy storage system by each component of the liquid-cooled energy storage system.

[0056] This invention improves the liquid cooling system for energy storage by employing an alumina ceramic water-cooled plate in a top-mounted, direct-contact configuration with the battery cells. Compared to the traditional bottom-mounted method, this results in significantly improved cooling efficiency. This design enhances heat transfer efficiency and more effectively controls the battery cell temperature, thereby improving the performance, lifespan, and safety of the energy storage system. Furthermore, intelligent thermal management is utilized to achieve refined thermal management through data analysis, modeling, and prediction. By dynamically adjusting the operating status of the liquid cooling unit, reducing power consumption, providing fault warnings and safety protection, and conducting statistical analysis and developing optimization strategies, the intelligent thermal management system can improve the efficiency, performance, and reliability of the energy storage system.

[0057] In specific applications, in some embodiments of the method of the present invention, the process of performing artificial intelligence control of the energy storage liquid cooling system can be referred to as follows:

[0058] Step 1: Improve the liquid-cooled energy storage system: Use an alumina ceramic liquid cooling plate. In a top-mounted manner, the liquid cooling plate of the energy storage liquid cooling system is directly attached to the top of the battery chip. A capillary network and thermally conductive material are deployed inside the liquid cooling plate. The capillary network is used to distribute the coolant and increase the surface area of ​​the liquid cooling plate to improve heat dissipation efficiency. The thermally conductive material is used to transfer heat from the battery chip to the capillary network and other parts of the liquid cooling plate.

[0059] Furthermore, step 1 may also include: configuring a liquid-cooled energy storage system, including a liquid-cooled chiller unit, liquid-cooled piping, high and low voltage wiring harnesses, and coolant. A heater is provided for the liquid-cooled chiller unit. In the liquid-cooled chiller unit, the coolant is cooled by a compressor, and the cooled coolant is then heated or its temperature is regulated by the heater. Temperature sensors and valves are deployed along the liquid-cooled piping, and the type of coolant is selected. The coolant may be an aqueous solution of ethylene glycol, etc.

[0060] By employing alumina ceramic materials and a top-mounted design, the energy storage liquid cooling plate can better meet thermal management requirements, improve heat dissipation, and reduce heat conduction paths and thermal resistance to some extent. This helps maintain a stable battery operating temperature and improves the performance and lifespan of the energy storage system.

[0061] Step 2: Collect thermal energy data from the liquid-cooled energy storage system using sensors. This thermal energy data includes battery temperature, liquid cooling plate temperature, and liquid cooling flow rate data. Process the thermal energy data.

[0062] Step 3: Analyze the processed thermal energy data to obtain analysis results. Based on the analysis results, obtain data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system.

[0063] Step 4: Based on data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system, construct and train an artificial intelligence prediction model. Utilize this model to predict future thermal trends by learning from historical and real-time data, and make corresponding decisions based on the current thermal state.

[0064] Step 5: Based on the thermal development trend predicted by the artificial intelligence prediction model and the decisions provided, formulate and implement corresponding thermal energy control strategies: adjust the operating parameters of the liquid-cooled energy storage system according to the predicted thermal development trend, and control the real-time regulation of the liquid-cooled energy storage system by each component of the liquid-cooled energy storage system.

[0065] Furthermore, in step 5, the various components of the liquid-cooled energy storage system are controlled to perform real-time regulation of the liquid-cooled energy storage system, including:

[0066] The workflow of controlling a liquid-cooled energy storage system includes:

[0067] Coolant is pumped or circulated to the liquid cooling plate through liquid cooling pipes, so that the capillary network on the liquid cooling plate can evenly distribute the coolant to the surface of the liquid cooling plate.

[0068] The heat from the battery chip is transferred to the liquid cooling plate by the thermally conductive material on the plate, while the coolant on the surface of the plate exchanges heat with the top of the battery chip, absorbing the heat.

[0069] The heated coolant is pumped to the liquid chiller unit through coolant piping. In the liquid chiller unit, the coolant is cooled by the compressor, and then heated or regulated by the heater.

[0070] The heat in the coolant is dissipated to the surrounding environment through the radiator in the liquid cooling unit;

[0071] After being cooled, the coolant is pumped back to the liquid cooling plate, and the cycle repeats to exchange heat.

[0072] The entire liquid cooling system's workflow is intelligently controlled using an artificial intelligence predictive model. Coolant is cyclically transferred to the liquid cooling plates, where it absorbs and releases heat through conduction and radiation / convection to maintain the battery chips at a suitable operating temperature. In this way, the liquid cooling system effectively dissipates heat from the battery chips, improving the performance and stability of the energy storage system.

[0073] Furthermore, remote control may include step 6: remotely monitoring the liquid-cooled energy storage system via the cloud and managing it through an artificial intelligence predictive model. This involves uploading data, analysis results, and model parameters from the liquid-cooled energy storage system to the cloud for storage and processing. The cloud enables centralized management and analysis of multiple systems, while also providing remote monitoring, maintenance, and upgrade capabilities.

[0074] This invention provides a device for controlling an energy storage liquid cooling system using artificial intelligence, comprising a configuration module, a data processing module, an analysis module, an artificial intelligence model module, and a control module.

[0075] Improved configuration module for liquid-cooled energy storage system: Utilizing an alumina ceramic liquid cooling plate, the liquid cooling plate of the energy storage system is directly attached to the top of the battery chips in a top-mounted manner. A capillary network and thermally conductive materials are deployed inside the liquid cooling plate. The capillary network distributes the coolant and increases the surface area of ​​the liquid cooling plate to improve heat dissipation efficiency. The thermally conductive materials transfer heat from the battery chips to the capillary network and other parts of the liquid cooling plate.

[0076] The data processing module collects thermal energy data from the liquid-cooled energy storage system via sensors. This thermal energy data includes battery temperature, liquid cooling plate temperature, and liquid cooling flow rate. The module then processes this thermal energy data.

[0077] The analysis module analyzes and processes the thermal energy data to obtain analysis results. Based on these results, data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system are obtained.

[0078] The artificial intelligence model module builds and trains an AI prediction model based on data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system. This model learns from historical and real-time data to predict future thermal trends and makes corresponding decisions based on the current thermal state.

[0079] Based on the thermal development trend predicted by the artificial intelligence prediction model and the decisions provided, the control module formulates and executes corresponding thermal energy control strategies: according to the predicted thermal development trend, it adjusts the operating parameters of the liquid-cooled energy storage system and controls the real-time regulation of the liquid-cooled energy storage system by each component of the liquid-cooled energy storage system.

[0080] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description in the method embodiment of the present invention, and will not be repeated here.

[0081] Similarly, the device of the present invention can be configured with an energy storage liquid cooling system, using an alumina ceramic liquid cooling plate, which is positioned at the top and directly contacts the positive and negative electrodes of the main heat-generating parts of the battery cell. Compared with the water cooling plate placed at the bottom of the battery cell, it has a higher cooling effect, mainly reflected in the following aspects:

[0082] 1. Increased contact area: The top-mounted design allows the water-cooling plate to directly contact the main heat-generating parts of the battery cell's positive and negative terminals, resulting in a larger contact area compared to the traditional bottom-mounted design. This allows for more effective heat transfer from the battery cell to the water-cooling plate, increasing heat transfer and dissipation efficiency.

[0083] 2. Shortened heat transfer path: The top-mounted design shortens the heat transfer path. Heat can be transferred only through the thermally conductive material between the electrodes and the liquid cooling plate inside the cell, without needing to pass through the cell's packaging structure. This reduces thermal resistance and improves heat transfer efficiency.

[0084] 3. Superior thermal conductivity: Alumina ceramic materials have high thermal conductivity, which can conduct and disperse heat more effectively than traditional metal water-cooling plates. Using alumina ceramic water-cooling plates can improve heat dissipation, more effectively reduce cell temperature, and improve the system's thermal management capabilities.

[0085] By using artificial intelligence models to predict thermal trends based on collected data from the battery cells, refined thermal management can be achieved. Compared to adjusting solely based on cell temperature, intelligent thermal management enables more precise and intelligent thermal management strategies in the following aspects:

[0086] 1. Dynamic Adjustment of Liquid Cooling Unit Operating Status: Intelligent thermal management can adjust the operating status of the liquid cooling unit in real time based on predicted cell conditions. Through analysis and prediction, the system can anticipate the temperature rise trend of the cells and the dynamic changes in heat generation and dissipation. Based on this information, the system can dynamically adjust the operating parameters of the liquid cooling unit, such as the coolant flow rate and temperature, to achieve more precise thermal management and control.

[0087] 2. Energy Saving and Reduced Power Consumption: Intelligent thermal management can rationally optimize the operation strategy of the liquid cooling unit based on model and prediction results to reduce power consumption. By finely controlling the operating status of the liquid cooling unit, the cell temperature is maintained within a suitable range, avoiding overcooling or overheating. This avoids unnecessary energy consumption, reduces system power consumption, and improves energy utilization efficiency.

[0088] 3. Fault Warning and Safety Protection: Intelligent thermal management can identify abnormal conditions in battery cells in a timely manner through models and predictions, and provide fault warnings and safety protection. The system can monitor the temperature, pressure, and other key indicators of the battery cells, compare real-time data with predicted results, and take immediate corresponding measures, such as alarms, shutdowns, or adjustments to control strategies, if an anomaly is detected, to ensure the safety and reliability of the system.

[0089] 4. Statistical Analysis and Optimization Strategies: Intelligent thermal management can utilize extensive cell data and historical records for statistical analysis and optimization strategy development. The system can learn and identify thermal characteristics and energy consumption under different operating conditions, thereby optimizing thermal management strategies and improving system performance and efficiency.

[0090] It should be noted that not all steps and modules in the above processes and device structures are mandatory; some steps or modules can be omitted as needed. The execution order of each step is not fixed and can be adjusted as required. The system structure described in the above embodiments can be a physical structure or a logical structure. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be jointly implemented by certain components in multiple independent devices.

[0091] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for controlling an energy storage liquid cooling system using artificial intelligence, characterized in that: include: Step 1: Improve the liquid-cooled energy storage system: Utilize an alumina ceramic liquid cooling plate, placing it directly on top of the battery chips. Deploy a capillary network and thermally conductive material inside the liquid cooling plate. The capillary network distributes the coolant and increases the surface area of ​​the liquid cooling plate, improving heat dissipation efficiency. The thermally conductive material transfers heat from the battery chips to the capillary network and other parts of the liquid cooling plate. The liquid-cooled energy storage system includes a liquid-cooled unit, liquid-cooled piping, high and low voltage wiring harnesses, and coolant. The liquid-cooled unit is equipped with a heater. In the liquid-cooled unit, the coolant is cooled by a compressor, and the cooled coolant is heated or regulated by the heater. Temperature sensors and valves are deployed according to the liquid-cooled piping, and the type of coolant is selected. Step 2: Collect thermal energy data from the liquid-cooled energy storage system using sensors. This thermal energy data includes battery temperature, liquid cooling plate temperature, and liquid cooling flow rate data. Process the thermal energy data. Step 3: Analyze the processed thermal energy data to obtain analysis results. Based on the analysis results, obtain data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system. Step 4: Based on data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system, construct and train an artificial intelligence prediction model. Utilize this model to predict future thermal trends by learning from historical and real-time data, and make corresponding decisions based on the current thermal state. Step 5: Based on the thermal development trend predicted by the artificial intelligence prediction model and the decisions provided, formulate and execute corresponding thermal energy control strategies: Adjust the operating parameters of the liquid-cooled energy storage system according to the predicted thermal development trend, and control the real-time regulation of the liquid-cooled energy storage system by various components, including: The workflow of controlling a liquid-cooled energy storage system includes: Coolant is pumped or circulated to the liquid cooling plate through liquid cooling pipes, so that the capillary network on the liquid cooling plate can evenly distribute the coolant to the surface of the liquid cooling plate. The heat from the battery chip is transferred to the liquid cooling plate by the thermally conductive material on the plate, while the coolant on the surface of the plate exchanges heat with the top of the battery chip, absorbing the heat. The heated coolant is pumped to the liquid chiller unit through coolant piping. In the liquid chiller unit, the coolant is cooled by a compressor, and then heated or regulated by a heater. The heat in the coolant is dissipated to the surrounding environment through the radiator in the liquid cooling unit; After being cooled, the coolant is pumped back to the liquid cooling plate, and the cycle repeats to exchange heat.

2. The method for controlling an energy storage liquid cooling system using artificial intelligence according to claim 1, characterized in that: It also includes step 6: remotely monitoring the liquid-cooled energy storage system via the cloud and managing the liquid-cooled energy storage system through artificial intelligence predictive models.

3. A device for controlling an energy storage liquid cooling system using artificial intelligence, characterized in that: It includes a configuration module, a data processing module, an analysis module, an artificial intelligence model module, and a control module. Improved configuration module for liquid-cooled energy storage system: Utilizing an alumina ceramic liquid cooling plate, the liquid cooling plate of the energy storage system is directly attached to the top of the battery chips in a top-mounted manner. A capillary network and thermally conductive materials are deployed inside the liquid cooling plate. The capillary network distributes the coolant and increases the surface area of ​​the liquid cooling plate to improve heat dissipation efficiency. The thermally conductive materials transfer heat from the battery chips to the capillary network and other parts of the liquid cooling plate. The liquid-cooled energy storage system includes a liquid-cooled unit, liquid-cooled piping, high and low voltage wiring harnesses, and coolant. The liquid-cooled unit is equipped with a heater. In the liquid-cooled unit, the coolant is cooled by a compressor, and the cooled coolant is heated or regulated by the heater. Temperature sensors and valves are deployed according to the liquid-cooled piping, and the type of coolant is selected. The data processing module collects thermal energy data from the liquid-cooled energy storage system via sensors. This thermal energy data includes battery temperature, liquid cooling plate temperature, and liquid cooling flow rate. The module then processes this thermal energy data. The analysis module analyzes and processes the thermal energy data to obtain analysis results. Based on these results, data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system are obtained. The artificial intelligence model module builds and trains an AI prediction model based on data on the operating status, heat distribution, and heat dissipation effect of the liquid-cooled energy storage system. This model learns from historical and real-time data to predict future thermal trends and makes corresponding decisions based on the current thermal state. Based on the thermal development trend predicted by the artificial intelligence prediction model and the decisions provided, the control module formulates and executes corresponding thermal energy control strategies: adjusting the operating parameters of the liquid-cooled energy storage system according to the predicted thermal development trend, and controlling the real-time regulation of the liquid-cooled energy storage system by various components, including: The workflow of controlling a liquid-cooled energy storage system includes: Coolant is pumped or circulated to the liquid cooling plate through liquid cooling pipes, so that the capillary network on the liquid cooling plate can evenly distribute the coolant to the surface of the liquid cooling plate. The heat from the battery chip is transferred to the liquid cooling plate by the thermally conductive material on the plate, while the coolant on the surface of the plate exchanges heat with the top of the battery chip, absorbing the heat. The heated coolant is pumped to the liquid chiller unit through coolant piping. In the liquid chiller unit, the coolant is cooled by a compressor, and then heated or regulated by a heater. The heat in the coolant is dissipated to the surrounding environment through the radiator in the liquid cooling unit; After being cooled, the coolant is pumped back to the liquid cooling plate, and the cycle repeats to exchange heat.

4. The device for an artificial intelligence-controlled energy storage liquid cooling system according to claim 3, characterized in that: It also includes a cloud module, which uses the cloud to remotely monitor the liquid-cooled energy storage system and manages the liquid-cooled energy storage system through artificial intelligence predictive models.

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