High-capacity mobile energy storage battery heat dissipation mechanism and heat dissipation method based on phase change heat dissipation technology

Abstract

The invention discloses a heat dissipation mechanism and a heat dissipation method for a high-capacity mobile energy storage battery based on a phase change heat dissipation technology, and relates to the field of battery temperature control, the heat dissipation mechanism comprises a battery control unit which is used as an energy storage main end, supports external equipment to carry out charging and discharging operations, and bears each functional module and functional unit, a control instruction is edited and issued; the active heat dissipation execution component is used for actively carrying out air cooling and liquid cooling heat dissipation behaviors on the battery control unit after being triggered; the phase change heat dissipation deployment component is used for being deployed to the battery control unit and absorbing waste heat when the battery control unit reaches the phase change temperature; according to the real-time temperature and load dynamic adjustment heat dissipation strategy, the operation condition of the battery is comprehensively evaluated, timely response to the problem is ensured, and the collection time period is divided in a user-defined manner, so that the data cycle can be more flexibly managed, the trend analysis is more accurate, subsequent analysis and comparison are facilitated, and a rich data basis is provided for deep learning.

Description

Technical Field

[0001] The present invention relates to the technical field of battery temperature control, and in particular to a heat dissipation mechanism and a heat dissipation method for a large-capacity mobile energy storage battery based on phase change heat dissipation technology. Background Art

[0002] Phase change heat dissipation battery is a battery technology that uses phase change materials to improve heat dissipation performance. Phase change materials can change their physical phase at a specific temperature. During the battery charging and discharging process, heat is generated inside the battery. Phase change materials can absorb excess heat when the temperature reaches its phase change point, thereby preventing the battery from overheating. Large-capacity mobile energy storage batteries are more likely to generate heat during the charging and discharging process because their charging and discharging capabilities are stronger than those of general energy storage batteries. If the heat dissipation is poor, the temperature rise may cause safety hazards such as battery overheating, fire or explosion. Effective heat dissipation can ensure that the battery operates within a safe temperature range.

[0003] However, many traditional heat dissipation solutions cannot adapt to changes in battery load in real time, lack flexibility in heat management, and are difficult to adjust in real time according to battery status and environmental changes, resulting in insufficient or excessive heat dissipation. The application of phase change materials is relatively simple, and there is a lack of reasonable detection of their usage status, which fails to give full play to their advantages in thermal management.

[0004] Many existing systems have delays in fault detection and early warning mechanisms, making it difficult to detect potential problems in a timely manner, which can easily lead to equipment damage or safety hazards. The ability to respond quickly to abnormal situations is insufficient and the risk is high. Data collection and analysis are usually scattered, and there is a lack of a unified platform to integrate and analyze data, resulting in a slow and unscientific decision-making process. There is a lack of effective storage and analysis of historical data, making it difficult to provide reliable data support for system optimization. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a large-capacity mobile energy storage battery heat dissipation mechanism and heat dissipation method based on phase change heat dissipation technology, which can effectively solve the problems of the prior art.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] The present invention discloses a large-capacity mobile energy storage battery heat dissipation mechanism based on phase change heat dissipation technology, comprising:

[0010] The battery control unit, as the energy storage master, supports charging and discharging operations of external devices, carries various functional modules and functional units, and edits and issues control instructions;

[0011] An active heat dissipation execution component is used to actively perform air cooling and liquid cooling to the battery control unit after being triggered;

[0012] A phase change heat dissipation deployment component is used to be deployed at the battery control unit to absorb waste heat when the battery control unit reaches the phase change temperature;

[0013] A data detection unit, used to collect the ambient temperature data, electric energy storage state data and phase change state data of the battery control unit in real time;

[0014] The storage module is used to store all collected and analyzed data, and store the adjustment data marked as positive feedback and its associated historical trend data set, supporting cloud backup;

[0015] The trend integration module is used to perform custom segmentation on the data detection unit collection period, obtain all the collected data within the preset period, and generate several period trend data sets;

[0016] The threshold analysis module is used to compare whether there are abnormalities in the data in a trend data set for a certain period of time according to the preset threshold;

[0017] The state prediction module is used to build an abnormal trend analysis model through a deep learning algorithm, input an abnormal period trend data set for prediction analysis, and output the current abnormal expression data based on several fault factors and the abnormal trend data of the preset future period, which are integrated into comprehensive abnormal data;

[0018] The parameter adjustment module is used to output the adjustment setting parameters of the functional components, functional modules and functional units associated with the fault factors based on the comprehensive abnormal data output by the state prediction module, and send them to the battery control unit for corresponding adjustment.

[0019] Furthermore, the data detection unit includes an ambient temperature detection module, an energy storage fluctuation detection module and a phase change state detection module. The ambient temperature detection module, the energy storage fluctuation detection module and the phase change state detection module are interactively connected via a wireless network. The ambient temperature detection module is used to obtain the real-time temperature of a preset working area of ​​the battery control unit. The energy storage fluctuation detection module is used to obtain the change state of the remaining stored electric energy under the charging and discharging state of the battery control unit. The phase change state detection module is used to obtain the material phase change state of the phase change heat dissipation deployment component.

[0020] Furthermore, the marking process of the adjustment data of the positive feedback of the storage module is: when the parameter adjustment module submits the adjustment data, the detection data response of the data detection unit in the next time period is obtained, and after being integrated into a trend data set by the trend integration module, it is submitted to the threshold analysis module for threshold analysis. When there is no abnormal feedback in the trend data set, the adjustment data is marked as positive feedback.

[0021] Furthermore, the trend integration module is interactively connected to the correlation analysis module via a wireless network, and the correlation analysis module is interactively connected to the storage module and the parameter adjustment module via a wireless network. The correlation analysis module is used to synchronously receive a trend data set of a certain time period of the trend integration module following the threshold analysis module, and obtain data access rights of the storage module, and based on a preset correlation standard, match the historical trend data set and its historical positive feedback adjustment parameters that meet the correlation standard of the trend data set of the current time period, and submit the historical positive feedback adjustment data to the parameter adjustment module as an adjustment reference. When the state prediction module has no output response within a preset time period, the parameter adjustment module directly applies the adjustment data.

[0022] Furthermore, the battery control unit is interactively connected to the active heat dissipation execution component, the phase change heat dissipation deployment component, the data detection unit and the parameter adjustment module through an electrical medium, the battery control unit is interactively connected to the storage module through a wireless network, the trend integration module is interactively connected to the data detection unit and the threshold analysis module through a wireless network, and the state prediction module is interactively connected to the threshold analysis module and the parameter adjustment module through a wireless network.

[0023] A large-capacity mobile energy storage battery heat dissipation method based on phase change heat dissipation technology comprises the following steps:

[0024] Step 1: Deploy phase change materials to the energy storage battery, and collect the temperature of the preset working area of ​​the energy storage battery, the change of electric energy under the charging and discharging state, and the state of the phase change material in real time;

[0025] Step 2: When the energy storage battery reaches the preset phase change temperature, the phase change material absorbs excess heat and automatically triggers the heat dissipation mechanism, activating air cooling and liquid cooling measures respectively;

[0026] Step 3: Through customized time segmentation, extract the data collected in each segmentation period and generate a trend data set, perform trend analysis, and match similar historical positive feedback adjustment data based on the current trend data set;

[0027] Step 4: Use the abnormal trend analysis model to predict and analyze the input abnormal period trend data, output the current abnormal expression data and future abnormal trends, output comprehensive abnormal data, and identify fault factors;

[0028] Step 5: Generate adjustment setting parameters based on the comprehensive abnormal data, identify the functional components, functional modules and functional units related to the fault factors, and send these parameters back to the energy storage battery for corresponding adjustments;

[0029] Step 6: Perform corresponding adjustments according to the adjustment parameters generated in step 5, and continuously monitor the performance of the energy storage battery after the adjustment;

[0030] Step 7: Store the collected data and preset adjustment data, evaluate the adjustment data, and if the evaluated positive feedback coefficient reaches the preset value, it will be marked as positive feedback adjustment data. Otherwise, an adjustment strategy will be generated and submitted to the management end.

[0031] Furthermore, the abnormal trend analysis model is constructed through a deep learning algorithm, which obtains the original preset working area temperature, the electric energy changes under the charging and discharging states, the state data of the phase change material and the corresponding positive feedback adjustment data as training samples, extracts a number of statistical features for standardization, and inputs the collected abnormal period trend data into the trained abnormal trend analysis model. The abnormal trend analysis model outputs the current abnormal state, determines whether it is a fault by defining a threshold, and outputs the current abnormal expression and trend prediction for the future time period.

[0032] Furthermore, in the evaluation process of the adjustment data in step 7, based on the detection data of the next cycle, a positive feedback coefficient based on the attenuation of several fault coefficients is obtained, and its calculation formula is:

[0033]

[0034] In the formula, P represents the positive feedback coefficient, D i Represents the detection data of the i-th item in the current cycle, T i represents the target value of the i-th item in the current cycle, C i represents the fault coefficient of the i-th item in the current cycle, n represents the total number of detection data items in the current cycle, Represents the attenuation factor of the failure coefficient.

[0035] Furthermore, the working logic of the calculation formula is: for each detection data item, calculate the difference between it and the target value, introduce the fault coefficient to adjust the difference, accumulate all adjusted differences to obtain the total contribution value, sum the weights of each difference adjustment to obtain the normalization factor, and obtain the positive feedback coefficient by dividing the accumulated total contribution value by the normalized factor.

[0036] (III) Beneficial effects

[0037] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0038] 1. Through the combination of active air cooling and liquid cooling with phase change materials, the heat dissipation strategy can be dynamically adjusted according to the real-time temperature and load, the heat dissipation efficiency can be improved, the ambient temperature, power state and phase change state can be monitored in real time, the operating status of the battery can be comprehensively evaluated, and problems can be responded to in a timely manner. The customized segmentation of the collection period can manage the data cycle more flexibly, making trend analysis more accurate, facilitating subsequent analysis and comparison, and providing a rich data foundation for deep learning.

[0039] 2. The abnormal trend analysis model built by the deep learning algorithm inputs the data of the abnormal period, analyzes the model, integrates various fault factors, effectively detects abnormalities in the collected data, and forms comprehensive abnormal data alarms and response measures, so as to predict potential system failures in advance, improve early warning capabilities, and provide guarantees for the stability of the system. Through fault adjustment feedback, the battery management system has self-healing and self-optimization capabilities, thereby avoiding major failures.

[0040] 3. By taking measures to conduct correlation analysis with historical data, when fault data is generated, the available adjustment data of similar fault data in the repository can be immediately called as an emergency treatment plan. In an emergency, it can be quickly put into use. In the case of a long period of unresponsiveness caused by excessive computational effort or other factors, the model can be automatically put into use and the adjustment data can be analyzed. By verifying the test results, available adjustment data can be obtained as matching samples. In this way, during long-term use, the correlation analysis and abnormal trend analysis models are also in a continuous learning process, thereby reducing limitations during use. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a schematic diagram of the framework of the heat dissipation mechanism of the large-capacity mobile energy storage battery in the present invention;

[0043] Figure 2 It is a schematic diagram of the process of the heat dissipation method of a large-capacity mobile energy storage battery in the present invention.

[0044] The numbers in the figure represent, respectively, 1. battery control unit; 2. active heat dissipation execution component; 3. phase change heat dissipation deployment component; 4. data detection unit; 41. ambient temperature detection module; 42. energy storage fluctuation detection module; 43. phase change state detection module; 5. storage module; 6. trend integration module; 7. threshold analysis module; 8. state prediction module; 9. parameter adjustment module; 10. correlation analysis module. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0046] The present invention will be further described below in conjunction with the embodiments.

[0047] Example 1

[0048] This embodiment is a large-capacity mobile energy storage battery heat dissipation mechanism based on phase change heat dissipation technology, such as Figure 1 As shown, including:

[0049] Battery control unit 1, as the energy storage master, supports charging and discharging operations of external devices, carries various functional modules and functional units, and edits and issues control instructions, integrating all functions into one, optimizing the simplicity of operation and management efficiency;

[0050] The active heat dissipation execution component 2 is used to actively perform air cooling and liquid cooling to the battery control unit 1 after being triggered, thereby improving the heat dissipation reaction speed, effectively reducing the device temperature, and ensuring the battery performance and safety;

[0051] The phase change heat dissipation deployment component 3 is used to be deployed at the battery control unit 1, and absorbs waste heat when the battery control unit 1 reaches the phase change temperature, thereby effectively utilizing the waste heat generated, improving energy utilization efficiency, and extending the service life of the equipment;

[0052] The data detection unit 4 is used to collect the ambient temperature data, electric energy storage state data and phase change state data of the battery control unit 1 in real time; the data detection unit 4 includes an ambient temperature detection module 41, an energy storage fluctuation detection module 42 and a phase change state detection module 43, and the ambient temperature detection module 41, the energy storage fluctuation detection module 42 and the phase change state detection module 43 are interactively connected through a wireless network, the ambient temperature detection module 41 is used to obtain the real-time temperature of the preset working area of ​​the battery control unit 1, the energy storage fluctuation detection module 42 is used to obtain the remaining stored electric energy change state of the battery control unit 1 under the charging and discharging state, and the phase change state detection module 43 is used to obtain the material phase change state of the phase change heat dissipation deployment component 3, so as to ensure the comprehensiveness and real-time nature of the data and provide an accurate basis for subsequent decision-making;

[0053] The storage module 5 is used to store all collected data and analyzed data, and store the adjustment data marked as positive feedback and its associated historical trend data set, and supports cloud backup; the marking process of the positive feedback adjustment data is: when the parameter adjustment module 9 submits the adjustment data, the detection data response of the data detection unit 4 in the next period is obtained, and after being integrated into a trend data set by the trend integration module 6, it is submitted to the threshold analysis module 7 for threshold analysis. When there is no abnormal feedback in the trend data set, the adjustment data is marked as positive feedback, and a data recording and analysis system is established, which supports cloud backup and enhances the security and traceability of the data;

[0054] The trend integration module 6 is used to perform custom segmentation on the data detection unit 4 collection period, obtain all the collected data within the preset period, generate several period trend data sets, and can flexibly and accurately analyze the trends of different time periods and formulate targeted adjustment strategies;

[0055] Threshold analysis module 7, used to compare whether there is anomaly in the data in a trend data set of a certain period according to the preset threshold, timely discover potential problems, and ensure the stable operation of the equipment;

[0056] The state prediction module 8 is used to build an abnormal trend analysis model through a deep learning algorithm, input an abnormal period trend data set for prediction analysis, output current abnormal expression data based on several fault factors and abnormal trend data of a preset future period, and integrate them into comprehensive abnormal data, thereby improving the accuracy of fault prediction, reducing potential risks, and providing guidance for maintenance;

[0057] The parameter adjustment module 9 is used to output the parameters of the functional components, functional modules and functional units related to the fault factors based on the comprehensive abnormal data output by the state prediction module 8, and send them to the battery control unit 1 for corresponding adjustment, thereby realizing automatic adjustment, improving the reaction speed and efficiency, and reducing the need for manual intervention;

[0058] The trend integration module 6 is interactively connected to the correlation analysis module 10 through a wireless network. The correlation analysis module 10 is interactively connected to the storage module 5 and the parameter adjustment module 9 through a wireless network. The correlation analysis module 10 is used to synchronously receive the trend data set of a certain period of time from the trend integration module 6 with the threshold analysis module 7, and obtain the data access permission of the storage module 5. Based on the preset correlation standard, the historical trend data set and its historical positive feedback adjustment parameters that meet the correlation standard of the trend data set of the current period are matched. The historical positive feedback adjustment data is submitted to the parameter adjustment module 9 as an adjustment reference. When the state prediction module 8 has no output response within the preset time period, the parameter adjustment module 9 directly proceeds. By applying the adjustment data and using the preset correlation standard to match the historical trend data set, it is possible to more effectively utilize the existing data resources, realize deep mining and value enhancement of the data, and use the historical positive feedback adjustment parameters as an adjustment reference, which can improve the scientificity and effectiveness of the adjustment decision to a certain extent, thereby improving the overall performance of the system. When the state prediction module 8 fails to output a response within the preset time, the parameter adjustment module 9 can automatically adjust to reduce human intervention, improve the system's self-regulation ability and response speed, and provide more comprehensive and accurate support for decision-making by integrating the analysis of real-time data and historical data, helping relevant personnel make better decisions.

[0059] As an implementation method in this embodiment, Figure 1 As shown, the battery control unit 1 is interactively connected with the active heat dissipation execution component 2, the phase change heat dissipation deployment component 3, the data detection unit 4 and the parameter adjustment module 9 through an electrical medium, the battery control unit 1 is interactively connected with the storage module 5 through a wireless network, the trend integration module 6 is interactively connected with the data detection unit 4 and the threshold analysis module 7 through a wireless network, and the state prediction module 8 is interactively connected with the threshold analysis module 7 and the parameter adjustment module 9 through a wireless network.

[0060] In the specific implementation of this embodiment, the battery control unit 1 performs charging and discharging behaviors and each module unit control behavior, the active heat dissipation execution component 2 actively dissipates heat for the battery control unit 1, the phase change heat dissipation deployment component 3 is deployed to the battery control unit 1, and absorbs heat when the phase change temperature is reached, the working environment temperature of the battery control unit 1 is collected by the ambient temperature detection module 41, the remaining power fluctuation of the battery control unit 1 under the charging and discharging state is obtained by the energy storage fluctuation detection module 42, the phase change state data of the phase change heat dissipation deployment component 3 is obtained by the phase change state detection module 43, the collected data is integrated by time periods by the trend integration module 6, and the threshold analysis module 7 is used to determine whether the integrated data is abnormal. If so, the parameter adjustment module 9 is input to output the current and future abnormal predictions of the battery control unit 1, and the parameter adjustment module 9 is used to make planning adjustments and submit them to the battery control unit 1. The correlation analysis module 10 calls the historical adjustment data of the storage module 5 as an adjustment reference, and when the state prediction module 8 does not respond, the historical solution is directly applied;

[0061] Furthermore, this embodiment ensures that the system can respond to abnormal situations in a timely manner by real-time monitoring of battery conditions and ambient temperature, and uses deep learning technology to predict faults, thereby improving the intelligent efficiency of the system, responding to potential problems more quickly, improving the reliability of adjustment data, enhancing the system's self-learning ability, and ensuring continuous optimization of system performance.

[0062] Example 2

[0063] In other aspects, this embodiment also provides a large-capacity mobile energy storage battery heat dissipation method based on phase change heat dissipation technology, such as Figure 2 As shown, the following steps are included:

[0064] Step 1: Deploy phase change materials to the energy storage battery, and collect the temperature of the preset working area of ​​the energy storage battery, the change of electric energy under the charging and discharging state, and the state of the phase change material in real time;

[0065] Step 2: When the energy storage battery reaches the preset phase change temperature, the phase change material absorbs excess heat and automatically triggers the heat dissipation mechanism, activating air cooling and liquid cooling measures respectively;

[0066] Step 3: Through customized time segmentation, extract the data collected in each segmentation period and generate a trend data set, perform trend analysis, and match similar historical positive feedback adjustment data based on the current trend data set;

[0067] Step 4: Use the abnormal trend analysis model to predict and analyze the input abnormal period trend data, output the current abnormal expression data and future abnormal trends, output comprehensive abnormal data, and identify fault factors; the abnormal trend analysis model is constructed through a deep learning algorithm, and the original preset working area temperature, the electric energy change under the charging and discharging state, the state data of the phase change material and the corresponding positive feedback adjustment data are obtained as training samples for training, and several statistical features are extracted for standardization. The collected abnormal period trend data is input into the trained abnormal trend analysis model, and the abnormal trend analysis model outputs the current abnormal state, determines whether it is a fault by defining a threshold, and outputs the current abnormal expression and trend prediction of the future period;

[0068] Step 5: Generate adjustment setting parameters based on the comprehensive abnormal data, identify the functional components, functional modules and functional units related to the fault factors, and send these parameters back to the energy storage battery for corresponding adjustments;

[0069] Step 6: Perform corresponding adjustments according to the adjustment parameters generated in step 5, and continuously monitor the performance of the energy storage battery after the adjustment;

[0070] Step 7: Store the collected data and preset adjustment data, evaluate the adjustment data, and if the evaluated positive feedback coefficient reaches the preset value, it will be marked as positive feedback adjustment data. Otherwise, an adjustment strategy will be generated and submitted to the management end.

[0071] Compared with the existing technology, through real-time data collection, trend data sets and abnormal trend analysis models are constructed, so that the system can make predictions and adjustments based on current and historical data. The abnormal trend analysis model is trained using deep learning algorithms to automatically identify and predict abnormalities, which helps to judge the fault status in time. The comprehensive abnormal data and adjustment parameters are fed back to the energy storage battery, and each functional component is adjusted according to the evaluation results to optimize the working performance. By evaluating the positive feedback coefficient of the adjustment data, it is ensured that the system can learn and optimize its own adjustment strategy to form a closed loop of self-improvement.

[0072] Example 3

[0073] In this embodiment, an evaluation and calculation method for adjusting data is provided. The specific evaluation process is: based on the detection data of the next cycle, a positive feedback coefficient based on the attenuation of several fault coefficients is obtained, and the calculation formula is:

[0074]

[0075] In the formula, P represents the positive feedback coefficient, which indicates the effectiveness of the adjustment data and the degree of influence on the system stability. The higher the value, the better the effectiveness of the adjustment data. i Represents the detection data of the i-th item in the current cycle, indicating the actual measurement value of a specific indicator, Ti represents the target value of the i-th item in the current cycle, which indicates the expected measurement value of the system under normal operation, C i represents the fault coefficient of the i-th item in the current cycle, which represents the degree of fault associated with the measured value. The larger the value, the more serious the fault. n represents the total number of detection data items in the current cycle, indicating the number of detection indicators to be evaluated. Represents the attenuation factor of the fault coefficient. The higher this item is, the smaller the contribution of the fault impact to the positive feedback coefficient is, and vice versa.

[0076] The working logic of the calculation formula is: for each detection data item, through (D i -T i ) calculates the difference between it and the target value. The target value is the ideal state that the system should reach when working normally. Therefore, this difference reflects the degree of deviation between the current state and the ideal state. The larger the difference value, the more serious the deviation from the target and the worse the system performance.

[0077] Introducing Failure Factor Adjust the difference. The fault coefficient indicates the degree of fault related to the detection index. Using the exponential decay method, the higher the fault coefficient, the more serious the fault is and the greater the attenuation effect is, which means that the contribution of this item to the positive feedback will be greatly reduced, reducing the interference of the error caused by the fault on the evaluation result.

[0078] All adjusted differences are added up to get the total contribution value, and the weight of each difference adjustment is calculated by The sum is taken to obtain the normalized factor, and the positive feedback coefficient is obtained by dividing the accumulated total contribution value by the normalized factor. This coefficient integrates the validity of all detection data and more truly reflects whether the submitted adjustment data has an actual positive impact on the system by reducing the impact of the fault.

[0079] In summary, the present invention deploys phase change materials in energy storage batteries. When the battery temperature reaches a preset phase change temperature, the heat dissipation mechanism is automatically triggered, and the temperature, charge and discharge status, and status of the phase change materials of the energy storage battery are collected in real time. The battery performance can be dynamically monitored and responded to in a timely manner, thereby ensuring the safety and stable operation of the battery. By extracting data in each segmentation cycle and performing trend analysis, positive feedback adjustment is performed based on the current trend set matching historical data, thereby improving the prediction and adjustment capabilities of the battery status, so that the battery can optimize performance under different working conditions;

[0080] The abnormal trend analysis model built using deep learning algorithms can predict and analyze the input abnormal data, identify fault factors, enhance the early warning capability of potential faults with intelligent analysis methods, and improve the self-healing ability of the system. According to the adjustment parameters generated by the comprehensive abnormal data, the corresponding functional adjustments of the energy storage battery can be made, which can effectively avoid the occurrence of faults and improve the overall reliability of the system. The performance after adjustment is continuously monitored and data is stored. Combined with the evaluation of positive and negative feedback coefficients, intelligent adjustment strategies are implemented, which not only optimizes the operating status of the battery, but also continuously improves system performance to form a virtuous circle.

[0081] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements 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 large-capacity mobile energy storage battery heat dissipation mechanism based on phase change heat dissipation technology, characterized in that: include: The battery control unit (1), as the energy storage master, supports charging and discharging operations of external devices, carries various functional modules and functional units, and edits and issues control instructions; An active heat dissipation execution component (2) is used to actively perform air cooling and liquid cooling on the battery control unit (1) after being triggered; A phase change heat dissipation deployment component (3) is used to be deployed at the battery control unit (1) and absorb waste heat when the battery control unit (1) reaches a phase change temperature; A data detection unit (4) is used to collect ambient temperature data, electric energy storage state data and phase change state data of the battery control unit (1) in real time; A storage module (5) is used to store all collected data and analyzed data, and to store adjustment data marked as positive feedback and its associated historical trend data set, and supports cloud backup; A trend integration module (6) is used to perform custom segmentation on the collection period of the data detection unit (4), obtain all collected data within a preset period, and generate a plurality of period trend data sets; A threshold analysis module (7) is used to compare whether there is any abnormality in the data in a trend data set for a certain period of time according to a preset threshold; A state prediction module (8) is used to construct an abnormal trend analysis model through a deep learning algorithm, input an abnormal period trend data set for prediction analysis, output current abnormal expression data based on a number of fault factors and abnormal trend data for a preset future period, and integrate them into comprehensive abnormal data; The parameter adjustment module (9) is used to output the parameters of the functional components, functional modules and functional units adjusted and set in relation to the fault factors based on the comprehensive abnormal data output by the state prediction module (8), and send them to the battery control unit (1) for corresponding adjustment.

2. According to claim 1, a large-capacity mobile energy storage battery heat dissipation mechanism based on phase change heat dissipation technology is characterized in that: The data detection unit (4) comprises an ambient temperature detection module (41), an energy storage fluctuation detection module (42) and a phase change state detection module (43); the ambient temperature detection module (41), the energy storage fluctuation detection module (42) and the phase change state detection module (43) are interconnected via a wireless network; the ambient temperature detection module (41) is used to obtain the real-time temperature of a preset working area of ​​the battery control unit (1); the energy storage fluctuation detection module (42) is used to obtain the remaining stored electric energy change state of the battery control unit (1) under the charging and discharging state; and the phase change state detection module (43) is used to obtain the material phase change state of the phase change heat dissipation deployment component (3).

3. According to claim 1, a large-capacity mobile energy storage battery heat dissipation mechanism based on phase change heat dissipation technology is characterized in that: The marking process of the positive feedback adjustment data of the storage module (5) is as follows: when the parameter adjustment module (9) submits the adjustment data, the detection data response of the data detection unit (4) in the next time period is obtained, and after being integrated into a trend data set by the trend integration module (6), it is submitted to the threshold analysis module (7) for threshold analysis. When there is no abnormal feedback in the trend data set, the adjustment data is marked as positive feedback.

4. According to claim 1, a large-capacity mobile energy storage battery heat dissipation mechanism based on phase change heat dissipation technology is characterized in that: The trend integration module (6) is interactively connected to the association analysis module (10) via a wireless network. The association analysis module (10) is interactively connected to the storage module (5) and the parameter adjustment module (9) via a wireless network. The association analysis module (10) is used to follow the threshold analysis module (7) to synchronously receive a certain period of time trend data set of the trend integration module (6), and obtain the data access rights of the storage module (5). Based on a preset association standard, the historical trend data set and its historical positive feedback adjustment parameters that meet the current period trend data set association standard are matched. The historical positive feedback adjustment data is submitted to the parameter adjustment module (9) as an adjustment reference. When the state prediction module (8) has no output response within a preset time period, the parameter adjustment module (9) directly applies the adjustment data.

5. According to claim 1, a large-capacity mobile energy storage battery heat dissipation mechanism based on phase change heat dissipation technology is characterized in that: The battery control unit (1) is interactively connected with the active heat dissipation execution component (2), the phase change heat dissipation deployment component (3), the data detection unit (4) and the parameter adjustment module (9) via an electrical medium; the battery control unit (1) is interactively connected with the storage module (5) via a wireless network; the trend integration module (6) is interactively connected with the data detection unit (4) and the threshold analysis module (7) via a wireless network; and the state prediction module (8) is interactively connected with the threshold analysis module (7) and the parameter adjustment module (9) via a wireless network.

6. A method for dissipating heat of a large-capacity mobile energy storage battery based on phase change heat dissipation technology, the method being an implementation method of a large-capacity mobile energy storage battery heat dissipation mechanism based on phase change heat dissipation technology according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: Deploy phase change materials to the energy storage battery, and collect the temperature of the preset working area of ​​the energy storage battery, the change of electric energy under the charging and discharging state, and the state of the phase change material in real time; Step 2: When the energy storage battery reaches the preset phase change temperature, the phase change material absorbs excess heat and automatically triggers the heat dissipation mechanism, activating air cooling and liquid cooling measures respectively; Step 3: Through customized time segmentation, extract the data collected in each segmentation period and generate a trend data set, perform trend analysis, and match similar historical positive feedback adjustment data based on the current trend data set; Step 4: Use the abnormal trend analysis model to predict and analyze the input abnormal period trend data, output the current abnormal expression data and future abnormal trends, output comprehensive abnormal data, and identify fault factors; Step 5: Generate adjustment setting parameters based on the comprehensive abnormal data, identify the functional components, functional modules and functional units related to the fault factors, and send these parameters back to the energy storage battery for corresponding adjustments; Step 6: Perform corresponding adjustments according to the adjustment parameters generated in step 5, and continuously monitor the performance of the energy storage battery after the adjustment; Step 7: Store the collected data and preset adjustment data, evaluate the adjustment data, and if the evaluated positive feedback coefficient reaches the preset value, it will be marked as positive feedback adjustment data. Otherwise, an adjustment strategy will be generated and submitted to the management end.

7. A large-capacity mobile energy storage battery heat dissipation method based on phase change heat dissipation technology according to claim 6, characterized in that: The abnormal trend analysis model is constructed through a deep learning algorithm, which obtains the original preset working area temperature, the electric energy change under the charging and discharging state, the state data of the phase change material and the corresponding positive feedback adjustment data as training samples for training, extracts a number of statistical features for standardization, and inputs the collected abnormal period trend data into the trained abnormal trend analysis model. The abnormal trend analysis model outputs the current abnormal state, determines whether it is a fault by defining a threshold, and outputs the current abnormal expression and trend prediction for the future time period.

8. The method for dissipating heat of a large-capacity mobile energy storage battery based on phase change heat dissipation technology according to claim 6 is characterized in that: In the evaluation process of the adjustment data in step 7, based on the detection data of the next cycle, a positive feedback coefficient based on the attenuation of several fault coefficients is obtained, and its calculation formula is: In the formula, P represents the positive feedback coefficient, D i Represents the detection data of the i-th item in the current cycle, T i represents the target value of the i-th item in the current cycle, C i represents the fault coefficient of the i-th item in the current cycle, n represents the total number of detection data items in the current cycle, Represents the attenuation factor of the failure coefficient.

9. A large-capacity mobile energy storage battery heat dissipation method based on phase change heat dissipation technology according to claim 8, characterized in that: The working logic of the calculation formula is: for each detection data item, calculate the difference between it and the target value, introduce the fault coefficient adjustment difference, accumulate all adjusted differences to obtain the total contribution value, sum the weights of each difference adjustment to obtain the normalization factor, and obtain the positive feedback coefficient by dividing the accumulated total contribution value by the normalized factor.

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