Liquid-cooled dynamic battery cluster temperature management protection device and temperature control method

Abstract

The invention discloses a liquid-cooled dynamic battery cluster temperature management protection device and a temperature control method, and relates to the field of battery temperature control, and the temperature management protection device comprises a liquid cooling control terminal which is used for butting and integrating an energy storage end of a plurality of battery units and providing circulating liquid cooling synchronous with the starting of the battery units, control instructions are edited, received and issued; the data acquisition module is used for dividing a liquid cooling circulation route into a plurality of sections and detecting temperature data of liquid in different sections in the charging and discharging state process of each battery unit; the temperature deviation of each section is identified in real time, a problem area is rapidly positioned, more comprehensive and accurate temperature distribution can be obtained, abnormal conditions are helped to be identified and positioned in real time, the response speed to transient load change is improved, it is ensured that the battery is kept in a safe temperature range in the rapid charging and discharging process, and the battery quality is improved. And fault hidden dangers caused by insufficient temperature monitoring capability are avoided.

Description

Technical Field

[0001] This invention relates to the field of battery temperature control technology, specifically to a liquid-cooled dynamic battery cluster temperature management and protection device and temperature control method. Background Technology

[0002] Liquid-cooled dynamic battery clusters, as a type of battery technology, combine the innovative design of liquid cooling and continuous movement of battery cells. Unlike traditional stationary battery systems, the battery cells are installed in a container filled with coolant. A liquid pump circulates the coolant around the container to absorb heat. A special mechanism allows the battery cells to move and rearrange as needed. The control system monitors the battery temperature and performance and adjusts cooling and movement to optimize operation. It has good development prospects in fields such as electric vehicles, energy storage systems, aerospace, and data centers.

[0003] However, current temperature management and protection solutions for battery clusters still have room for improvement. Traditional technologies typically rely on a few fixed sensors to monitor the temperature of the battery cluster, which can easily lead to an inaccurate reflection of the temperature distribution of the entire battery cluster and make it difficult to accurately diagnose the root cause behind temperature anomalies. They usually lack the ability to actively control the temperature and lack flexibility in controlling the liquid cooling system. When responding to transient load changes, they are not easy to adjust in time. As a result, if the battery temperature exceeds the safe range during rapid charging and discharging, it is difficult to adjust the cooling strategy based on real-time data, causing a slow recovery. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention provides a liquid-cooled dynamic battery cluster temperature management and protection device and temperature control method, which can effectively solve the problems of the prior art.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] This invention discloses a liquid-cooled dynamic battery cluster temperature management and protection device, comprising:

[0009] The liquid-cooled control terminal is used to interface with an energy storage terminal that integrates several battery cells, providing cyclic liquid cooling that is synchronized with the start-up of the battery cells, and editing, receiving and sending control commands.

[0010] The data acquisition module is used to divide the liquid cooling circulation route into several sections and detect the temperature data of the liquid in different sections during the charging and discharging process of each battery cell.

[0011] The standard temperature spectrum module is used to obtain standard temperature change spectrum for each segment corresponding to different charge and discharge states, and extract the standard temperature range for each segment from the spectrum based on the current charge and discharge state.

[0012] The temperature deviation detection module is used to compare the collected temperature data with the standard temperature range, detect whether there is a temperature deviation, identify the segment that exceeds the standard temperature range, and calculate the temperature deviation.

[0013] The deviation calculation module is used to analyze the way temperature deviation changes, calculate the abnormal coefficient of liquid cooling capacity of liquid cooling control terminal and abnormal coefficient of heat release of battery cluster, and obtain the abnormal content set.

[0014] The anomaly analysis module is used to build an anomaly identification model, analyze the anomaly set through the anomaly identification model, identify the anomaly causes of the anomaly-related components and their settings, and output an anomaly cause set containing several anomaly factors.

[0015] The adjustment instruction module is used to edit the adjustment data for each component and its equipment content, based on the set of abnormal causes.

[0016] The instruction execution module is used to receive adjustment data submitted to the liquid cooling control terminal in the current or next working cycle for instruction execution, and to receive feedback data.

[0017] Furthermore, the standard temperature spectrum module has sub-modules, including a threshold analysis module, an active setting module, and a configuration module. These modules are interconnected via a wireless network.

[0018] The threshold analysis module is used to obtain the current charging and discharging status of each battery cell and the working status of the liquid cooling components, and dynamically update the standard temperature range of each segment.

[0019] The active setting module is used to select and perform manual secondary settings based on the standard temperature range output by the threshold analysis module;

[0020] The configuration module is used to receive and configure the submitted original standard temperature range or the standard temperature range after secondary settings.

[0021] Furthermore, the anomaly analysis module is interconnected with an alarm module via a wireless network. The alarm module is used to edit alarm commands. After the anomaly analysis module obtains the anomaly cause set output, it edits the alarm command and feeds it directly back to the command execution module. The command execution module immediately submits the alarm to the liquid cooling control terminal in the current working cycle.

[0022] Furthermore, the liquid-cooled control terminal and the data acquisition module are interconnected via an electrical medium; the data acquisition module and the standard temperature spectrum module are interconnected via an electrical medium; the standard temperature spectrum module and the temperature deviation detection module are interconnected via a wireless network; the deviation calculation module and the anomaly analysis module are interconnected via a wireless network; the anomaly analysis module and the adjustment command module are interconnected via a wireless network; and the command execution module is interconnected with the liquid-cooled control terminal and the adjustment command module via a wireless network.

[0023] A liquid-cooled dynamic battery cluster temperature control method includes the following steps:

[0024] Step 1: Divide the battery cluster into several cooling sections based on its structure and cooling requirements;

[0025] Step 2: Monitor the temperature of the liquid in different sections in real time, organize and store the collected temperature data, and correlate it with the current charging and discharging state of the battery cluster;

[0026] Step 3: Based on the parameters of the battery cluster and the thermal characteristics under different charging and discharging states, calculate and plot the corresponding standard temperature change spectrum as a benchmark for judging whether the temperature is normal.

[0027] Step 4: Compare the real-time monitored temperature data with the standard temperature change graph for the corresponding charge and discharge states to determine if there is a temperature deviation.

[0028] Step 5: Analyze the detected temperature deviation and calculate the liquid cooling capacity coefficient and heat release anomaly coefficient;

[0029] Step 6: If there is a temperature deviation, use fault tree analysis to find the components associated with the deviation, perform fault analysis, and based on the fault analysis results, output the corresponding adjustment instructions and submit them to the control terminal.

[0030] Step 7: After implementing the adjustment instructions, continuously monitor the temperature changes and system response of each section, and report the completion status of the adjustment to the control terminal.

[0031] Furthermore, the cooling section comprises: cooling pipes, a liquid pump, a flow control valve, a heat exchanger, a temperature sensor, and a cooling medium.

[0032] Furthermore, in the comparison process in step 4, the real-time monitored temperature data is plotted as a real-time temperature change graph, and then compared with the standard temperature change graph to identify the number and value of deviation points. Deviation points that exceed the preset deviation data threshold are marked and real-time feedback is provided.

[0033] Furthermore, the calculation process of the liquid cooling capacity coefficient in step 5 is as follows: obtain the cooling medium flow rate, specific heat capacity, temperature change coefficient and density, and calculate the cooling capacity coefficient per unit flow rate. The calculation process of the abnormal heat release coefficient is as follows: obtain the normal heat release value under standard charge and discharge conditions, monitor the actual heat release under the current charge and discharge conditions, calculate the difference between the normal heat release value and the actual heat release value, scalarize the comparison between abnormal heat and normal heat, and obtain the abnormal heat release coefficient generated during the charge and discharge process.

[0034] Furthermore, the working logic of the fault tree analysis method in step 6 is as follows:

[0035] Identify the main failures of battery components and liquid cooling cycles as top events. Analyze the causes of each main failure, list potential sub-event failures, and construct a fault tree based on the relationships between the failures. The fault tree includes:

[0036] AND gate: The top event of the output will only occur if all the child events of the input have occurred;

[0037] OR gate: The top event of the output will occur as long as one of the input sub-events occurs;

[0038] Specifically, by assessing the probability of each sub-event occurring, and then calculating the probability of the top event occurring based on logical relationships, components whose occurrence probability exceeds a preset occurrence threshold are identified as high-risk components, and high-risk components are given priority for analysis.

[0039] (III) Beneficial Effects

[0040] Compared with known prior art, the technical solution provided by this invention has the following beneficial effects:

[0041] 1. By segmenting the liquid circulation area, a standard temperature change map is generated, and real-time temperature data is compared and analyzed with it. This allows for real-time identification of temperature deviations in each segment, rapid location of problem areas, and timely implementation of measures. This reduces the probability of failure and shortens maintenance time. It provides a more comprehensive and accurate temperature distribution, helps to identify and locate abnormal situations in real time, improves the response speed to transient load changes, ensures that the battery remains within a safe temperature range during rapid charging and discharging, and avoids potential failures due to insufficient temperature monitoring capabilities.

[0042] 2. By combining real-time monitoring data with the calculated liquid cooling capacity coefficient and heat release anomaly coefficient, the device can flexibly adjust the cooling strategy, manage the flow and temperature of the liquid cooling medium according to the actual situation, improve cooling efficiency, avoid battery overheating, and continuously monitor temperature changes and system response after implementing adjustment commands, and feed the results back to the control terminal to ensure that the system can respond quickly to dynamic changes, realize closed-loop management, and continuously optimize and adjust the control strategy based on feedback, thereby improving overall performance.

[0043] 3. By using fault tree analysis to identify risky components and analyze the causes of failures, the process of constructing a fault tree can help identify the logical relationships between components at the lower level, focus on high-risk components for in-depth analysis, and through systematic analysis, effectively predict and eliminate potential failures, reduce human error and response time, and improve the reliability and security of the system. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0045] Figure 1 This is a schematic diagram of the framework of the liquid-cooled dynamic battery cluster temperature management and protection device in this invention.

[0046] Figure 2 This is a schematic flowchart of the liquid-cooled dynamic battery cluster temperature control method of the present invention.

[0047] The labels in the diagram represent: 1. Liquid cooling control terminal; 2. Data acquisition module; 3. Standard temperature spectrum module; 31. Threshold analysis module; 32. Active setting module; 33. Configuration module; 4. Temperature deviation detection module; 5. Deviation calculation module; 6. Anomaly analysis module; 7. Adjustment command module; 8. Command execution module; 9. Alarm module. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0049] The present invention will be further described below with reference to embodiments.

[0050] Example 1

[0051] This embodiment provides a liquid-cooled dynamic battery cluster temperature management and protection device, such as... Figure 1 As shown, it includes:

[0052] Liquid cooling control terminal 1 is used to interface with an energy storage terminal that integrates several battery cells, provide cyclic liquid cooling that is synchronized with the start-up of the battery cells, and edit, receive and send control commands.

[0053] Data acquisition module 2 is used to divide the liquid cooling circulation route into several sections. During the charging and discharging process of each battery cell, it detects the temperature data of the liquid in different sections to obtain a more comprehensive view of the temperature distribution of the battery cluster, which enhances the monitoring of the temperature status and facilitates early warning.

[0054] The standard temperature spectrum module 3 is used to acquire standard temperature change spectra for different charge and discharge states in various segments. Based on the current charge and discharge state, it extracts the standard temperature range for each segment from the spectrum to generate a targeted standard temperature range. The standard temperature spectrum module 3 has sub-modules, including a threshold analysis module 31, an active setting module 32, and a configuration module 33. These modules are interconnected via a wireless network.

[0055] The threshold analysis module 31 is used to obtain the current charging and discharging status of each battery cell and the working status of the liquid cooling element, and dynamically update the standard temperature range of each segment to maintain the realism and accuracy of the temperature standard.

[0056] The active setting module 32 is used to select and manually customize secondary settings based on the standard temperature range output by the threshold analysis module 31, so that users can flexibly adjust the standard temperature range according to actual needs.

[0057] Configuration module 33 is used to receive and configure the submitted original standard temperature range or the standard temperature range after secondary setting, integrate and manage the temperature range setting, and ensure the consistency of the system.

[0058] Temperature deviation detection module 4 is used to compare the collected temperature data with the standard temperature range, detect whether there is a temperature deviation, identify the segment that exceeds the standard temperature range, and calculate the temperature deviation. Through comparative analysis, it can quickly identify the temperature deviation and promptly discover potential overheating or undercooling segments.

[0059] Deviation calculation module 5 is used to analyze the way temperature deviation changes, calculate the abnormal coefficient of liquid cooling capacity of liquid cooling control terminal 1 and the abnormal coefficient of heat release of battery cluster, obtain the abnormal content set, objectively evaluate the cooling performance of the system by calculating the abnormal coefficients of liquid cooling capacity and heat release, and obtain a detailed abnormal content set so that subsequent processing is targeted.

[0060] Anomaly analysis module 6 is used to build anomaly identification model. By analyzing the anomaly set through the anomaly identification model, the abnormal causes of the abnormal components and their settings are identified, and the anomaly cause set containing several abnormal factors is output. By building a complex anomaly identification model, various abnormal factors are automatically identified and analyzed, thereby optimizing the system fault diagnosis and repair process, improving the system's intelligence level, and reducing human intervention.

[0061] The adjustment instruction module 7 is used to edit the adjustment data of each component and its equipment content with reference to the set of abnormal causes. Based on the abnormal causes, it generates corresponding adjustment data to provide a basis for the automatic adjustment of the system and ensure the safety and performance optimization of the battery.

[0062] The instruction execution module 8 is used to receive adjustment data submitted to the liquid cooling control terminal 1 in the current or next working cycle for instruction execution, and to receive feedback data to ensure rapid response and execution of adjustment data, thereby enhancing the system's real-time performance and flexibility. Simultaneously, the receipt of feedback data provides important information for further optimization.

[0063] The anomaly analysis module 6 is wirelessly connected to the alarm module 9. The alarm module 9 is used to edit alarm commands. After the anomaly analysis module 6 obtains the anomaly cause set output, it edits the alarm command and feeds it directly back to the command execution module 8. The command execution module 8 immediately submits the alarm to the liquid cooling control terminal 1 in the current working cycle. After anomaly identification, it quickly issues an alarm command to promptly remind the operator to intervene, thereby enhancing the safety and reliability of the system.

[0064] As one implementation method in this embodiment, such as Figure 1 As shown, the liquid cooling control terminal 1 is electrically connected to the data acquisition module 2, the data acquisition module 2 is electrically connected to the standard temperature spectrum module 3, the standard temperature spectrum module 3 is wirelessly connected to the temperature deviation detection module 4, the deviation calculation module 5 and the anomaly analysis module 6 are wirelessly connected, the anomaly analysis module 6 and the adjustment command module 7 are wirelessly connected, and the command execution module 8 is wirelessly connected to the liquid cooling control terminal 1 and the adjustment command module 7.

[0065] In this specific implementation, the liquid cooling control terminal 1 connects to the battery system to provide liquid cooling circulation behavior. The data acquisition module 2 collects liquid cooling data and battery data. The standard temperature spectrum module 3 formulates a standard temperature spectrum. The standard temperature is set by the threshold analysis module 31, and manual adjustment is intervened by the active setting module 32. Integrated configuration is performed by the configuration module 33. The temperature deviation detection module 4 identifies the sections with abnormal temperatures. The deviation calculation module 5 calculates the difference between the actual temperature and the standard temperature. The anomaly analysis module 6 analyzes the specific fault cause. The alarm module 9 sends an alarm. The adjustment command module 7 generates adjustment commands based on the fault cause and the battery system and liquid cooling circulation device. The command execution module 8 executes the commands and provides feedback on the completion rate as an optimization reference for the anomaly analysis module 6 and the adjustment command module 7.

[0066] Compared with existing technologies, through close collaboration among various modules, efficient and precise temperature management is achieved, ensuring the optimal operating state of the liquid cooling system. Each module has optimized response speed, management accuracy, and safety, making the temperature management of dynamic battery clusters more intelligent and automated.

[0067] Example 2

[0068] At other levels, this embodiment also provides a liquid-cooled dynamic battery cluster temperature control method, such as... Figure 2 As shown, it includes the following steps:

[0069] Step 1: Divide the battery cluster into several cooling sections according to its structure and cooling requirements; the cooling sections consist of: cooling pipes, liquid pumps, flow control valves, heat exchangers, temperature sensors, and cooling media;

[0070] Step 2: Monitor the temperature of the liquid in different sections in real time, organize and store the collected temperature data, and correlate it with the current charging and discharging state of the battery cluster;

[0071] Step 3: Based on the parameters of the battery cluster and the thermal characteristics under different charging and discharging states, calculate and plot the corresponding standard temperature change spectrum as a benchmark for judging whether the temperature is normal.

[0072] Step 4: Compare the real-time monitored temperature data with the standard temperature change graph of the corresponding charge and discharge state to determine whether there is a temperature deviation. During the comparison process, the real-time monitored temperature data is plotted as a real-time temperature change graph, and then compared with the standard temperature change graph to identify the number and value of deviation points. Deviation points that exceed the preset deviation data threshold are marked and real-time feedback is provided.

[0073] Step 5: Analyze the detected temperature deviation and calculate the liquid cooling capacity coefficient and the heat release anomaly coefficient. The calculation process for the liquid cooling capacity coefficient is as follows: obtain the cooling medium flow rate, specific heat capacity, temperature change coefficient, and density, and calculate the cooling capacity coefficient per unit flow rate. The calculation process for the heat release anomaly coefficient is as follows: obtain the normal heat release value under standard charge and discharge conditions, monitor the actual heat release under the current charge and discharge conditions, calculate the difference between the normal heat release value and the actual heat release value, scalarize the comparison between the abnormal heat and the normal heat, and obtain the abnormal heat release coefficient generated during the charge and discharge process. If the liquid cooling capacity coefficient is insufficient to offset the actual heat release, it indicates that heat accumulation has occurred, leading to battery overheating. If the abnormal heat is much higher than the expected level, it indicates that there is excessive heat release, which is caused by an internal fault.

[0074] Step 6: If there is a temperature deviation, use fault tree analysis to find the components associated with the deviation, perform fault analysis, and based on the fault analysis results, output the corresponding adjustment instructions and submit them to the control terminal.

[0075] Step 7: After implementing the adjustment instructions, continuously monitor the temperature changes and system response of each section, and report the completion status of the adjustment to the control terminal.

[0076] Compared with existing technologies, by dividing the battery cluster into multiple cooling sections according to its structure and cooling requirements, the cooling effect can be optimized in a targeted manner, improving the overall thermal management efficiency of the system. Real-time monitoring of the temperature of different sections and correlation with the battery's charge and discharge status helps to detect abnormal temperatures in a timely manner, accurately determine the system's operating status, and provide an effective benchmark for temperature monitoring based on the standard temperature change spectrum drawn according to the charge and discharge status, thus promoting the scientific and systematic nature of temperature judgment.

[0077] By comparing real-time temperature data with standard graphs, temperature deviations can be quickly identified. The visualized real-time temperature change graphs enhance the intuitiveness of monitoring and judgment. Through specific mathematical analysis to calculate cooling capacity and heat release anomaly coefficients, the thermal dynamic behavior of the temperature control system can be deeply understood, providing a scientific basis for problem localization. By using fault tree analysis to find components associated with temperature deviations, fault analysis can be effectively performed and targeted adjustment instructions can be proposed, enhancing the fault diagnosis capability of the temperature control system. After implementing adjustment instructions, temperature changes and system response are continuously monitored, and the adjustment completion status is fed back to the control end, ensuring the effectiveness and timeliness of dynamic adjustments.

[0078] Example 3

[0079] In this embodiment, a fault tree analysis method is provided, the specific working logic of which is as follows:

[0080] Identify the main failures of battery components and liquid cooling cycles as top events. Analyze the causes of each main failure, list potential sub-event failures, and construct a fault tree based on the relationships between the failures. The fault tree includes:

[0081] AND gate: The top event of the output will only occur if all the child events of the input have occurred;

[0082] OR gate: The top event of the output will occur as long as one of the input sub-events occurs;

[0083] Specifically, by assessing the probability of each sub-event occurring, and then calculating the probability of the top event occurring based on logical relationships, components whose occurrence probability exceeds a preset occurrence threshold are identified as high-risk components, and high-risk components are given priority for analysis.

[0084] In this embodiment, by identifying the main faults of battery components and liquid cooling cycles, the core issues and objectives of the analysis are clarified, making the fault analysis more focused and efficient. A fault tree is constructed, and through the logical structure of AND and OR gates, the relationships between fault factors can be intuitively displayed, facilitating the understanding and analysis of complex fault situations. Potential sub-event faults are listed, helping to comprehensively identify various factors that may lead to the top event, preventing and mitigating the impact of faults in advance. The probability of each sub-event is assessed, and the probability of the top event is calculated through logical relationships, giving the fault analysis a quantitative basis and aiding in decision-making. By identifying components whose probability of occurrence exceeds a preset threshold, resources and attention are intelligently focused on high-risk components, optimizing fault management and maintenance strategies.

[0085] In summary, this invention acquires temperature data in real time through segmented monitoring of the liquid cooling cycle, ensuring that the device can respond quickly to the state of the battery cell. It generates a standard temperature change spectrum based on different charge and discharge states, compares the temperature data with the standard range in real time, and realizes dynamic updating and adaptive adjustment of the temperature range, ensuring that the battery cell operates in the optimal working environment.

[0086] Temperature deviation comparison analysis can quickly identify problem areas and mark the degree of deviation, which helps to accurately locate potential fault points. The introduction of calculation methods for liquid cooling capacity coefficient and heat release anomaly coefficient provides the system with quantitative analysis tools, which can more effectively evaluate the performance of the cooling system and the thermal management status of the battery. Fault tree analysis can identify relevant components, which helps to quickly troubleshoot and analyze system faults and speed up fault handling. The anomaly identification model can automatically analyze temperature deviations, optimize fault troubleshooting, reduce manual intervention, improve the intelligence level of the system, identify potential risks in advance, and quickly instruct operators to take measures through the alarm system.

[0087] Based on the cause of the fault, an adjustment command is output. After the adjustment command is implemented, the temperature change and system response are continuously monitored to form a closed-loop feedback mechanism. This allows for timely verification of the effectiveness of the adjustment measures, ensuring the dynamic adaptability of the temperature control system, effectively reducing the risk of battery overheating, and improving the overall safety and lifespan of the battery pack.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A liquid-cooled dynamic battery cluster temperature management and protection device, characterized in that: include: A liquid cooling control terminal (1) is used to connect to an energy storage terminal integrating a plurality of battery units, provide circulating liquid cooling synchronized with the start-up of the battery units, and edit, receive and issue control instructions; The data acquisition module (2) is used to divide the liquid cooling circulation route into several sections, and detect the temperature data of the liquid in different sections during the charging and discharging state of each battery unit; A standard temperature spectrum module (3) is used to obtain a standard temperature variation spectrum of each section corresponding to different charge and discharge states, and extract a standard temperature range of each section from the spectrum according to the current charge and discharge state; A temperature deviation detection module (4) is used to compare the collected temperature data with the standard temperature range, detect whether there is a temperature deviation, identify the section that exceeds the standard temperature range, and calculate the temperature deviation; A deviation calculation module (5) is used to analyze the way in which the temperature deviation changes, calculate the abnormal coefficient of the liquid cooling capacity of the liquid cooling control terminal (1) and the abnormal coefficient of heat release of the battery cluster, and obtain an abnormal content set; An abnormality analysis module (6) is used to construct an abnormality identification model, analyze an abnormality set through the abnormality identification model, identify abnormal causes of abnormal components and their settings, and output an abnormality cause set containing a number of abnormal factors; An adjustment instruction module (7) is used to edit the adjustment data corresponding to each component and its equipment content with reference to the abnormal cause set; The instruction execution module (8) is used to receive the adjustment data and submit it to the liquid cooling control terminal (1) for instruction execution in the current or next working cycle, and receive feedback data.

2. A liquid-cooled dynamic battery cluster temperature management and protection device according to claim 1, characterized in that: The standard temperature map module (3) is provided with submodules at the lower level, including a threshold analysis module (31), an active setting module (32) and a configuration module (33), wherein the threshold analysis module (31), the active setting module (32) and the configuration module (33) are interactively connected via a wireless network, wherein: A threshold analysis module (31) is used to obtain the current charge and discharge status of each battery unit and the working status of the liquid cooling element, and dynamically update and output the standard temperature range of each section; An active setting module (32) is used to select a manually customized secondary setting based on the standard temperature range output by the threshold analysis module (31); The configuration module (33) is used to receive and configure the submitted original standard temperature range or the standard temperature range that has been set for the second time.

3. The liquid-cooled dynamic battery cluster temperature management and protection device according to claim 1, characterized in that: The abnormality analysis module (6) is interactively connected to the alarm module (9) via a wireless network. The alarm module (9) is used to edit the alarm instruction. After the abnormality analysis module (6) obtains the output of the abnormality cause set, the alarm instruction is edited and directly fed back to the instruction execution module (8). The instruction execution module (8) immediately submits it to the liquid cooling control terminal (1) for alarm in the current working cycle.

4. The liquid-cooled dynamic battery cluster temperature management and protection device according to claim 1, characterized in that: The liquid cooling control terminal (1) is interactively connected to the data acquisition module (2) via an electrical medium, the data acquisition module (2) is interactively connected to the standard temperature map module (3) via an electrical medium, the standard temperature map module (3) is interactively connected to the temperature deviation detection module (4) via a wireless network, the deviation calculation module (5) and the abnormality analysis module (6) are interactively connected via a wireless network, the abnormality analysis module (6) and the adjustment instruction module (7) are interactively connected via a wireless network, and the instruction execution module (8) is interactively connected to the liquid cooling control terminal (1) and the adjustment instruction module (7) via a wireless network.

5. A liquid-cooled dynamic battery cluster temperature control method, the method is based on a liquid-cooled dynamic battery cluster temperature management and protection device according to any one of claims 1 to 4, characterized in that: The following steps are involved: Step 1: Divide the battery cluster into several cooling zones according to its structure and cooling requirements; Step 2: Monitor the temperature of the liquid in different sections in real time, organize and store the collected temperature data, and associate it with the current charge and discharge status of the battery cluster; Step 3: Calculate and draw the corresponding standard temperature change map based on the parameters of the battery cluster and the thermal characteristics under different charge and discharge states as a benchmark for judging whether the temperature is normal; Step 4: Compare the real-time monitored temperature data with the standard temperature change map of the corresponding charging and discharging state to determine whether there is a temperature deviation; Step 5: Analyze the detected temperature deviation and calculate the liquid cooling capacity coefficient and the heat release abnormality coefficient; Step 6: If there is a temperature deviation, find the components associated with the deviation through the fault tree analysis method, perform fault analysis, and output the corresponding adjustment instructions based on the fault analysis results and submit them to the control end; Step 7: After implementing the adjustment instructions, continue to monitor the temperature changes and system responses of each section, and feedback the adjustment completion to the control end.

6. A liquid-cooled dynamic battery cluster temperature control method according to claim 5, characterized in that: The cooling section is composed of: a cooling pipeline, a liquid pump, a flow control valve, a heat exchanger, a temperature sensor and a cooling medium.

7. A liquid-cooled dynamic battery cluster temperature control method according to claim 5, characterized in that: During the comparison process in step 4, the real-time monitored temperature data is plotted as a real-time temperature change map, and then matched with the standard temperature change map to identify the number and value of deviation points, mark the deviation points that exceed the preset deviation data threshold, and provide real-time feedback.

8. The liquid-cooled dynamic battery cluster temperature control method according to claim 5, characterized in that: The calculation process of the liquid cooling capacity coefficient in step 5 is: obtaining the cooling medium flow rate, specific heat capacity, temperature variation coefficient and density, and calculating the cooling capacity coefficient under unit flow rate. The calculation process of the abnormal heat release coefficient is: obtaining the conventional heat release value under the standard charging and discharging state, monitoring the actual heat release under the current charging and discharging state, calculating the difference between the conventional heat release value and the actual heat release value, scalarizing the comparison between the abnormal heat and the conventional heat, and obtaining the abnormal heat release coefficient generated during the charging and discharging process.

9. The liquid-cooled dynamic battery cluster temperature control method according to claim 5, characterized in that: The working logic of the fault tree analysis method in step 6 is: Identify the main faults of battery component failure and liquid cooling cycle as the top event, analyze the cause of each main fault, list the potential sub-event faults, and build a fault tree based on the relationship between the faults. The fault tree content includes: AND gate: The top event of the output will occur only when all the sub-events of the input occur; OR gate: As long as one of the input sub-events occurs, the output top event will occur; Among them, by evaluating the probability of occurrence of each sub-event, and then calculating the probability of occurrence of the top event with a logical relationship, the components whose probability of occurrence exceeds the preset occurrence threshold are identified and determined as high-risk components, and high-risk components are given priority for analysis.

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