A heat dissipation mechanism and method for a large-capacity mobile energy storage battery based on phase change heat dissipation technology

Abstract

The present invention discloses a heat dissipation mechanism and a heat dissipation method for a large-capacity mobile energy storage battery based on phase change heat dissipation technology, which relates to the field of battery temperature control. The heat dissipation mechanism includes: a battery control unit, serving as the main energy storage terminal, supporting external devices for charging and discharging operations, carrying various functional modules and functional units, and editing and issuing control instructions; an active heat dissipation execution component, used for actively performing air-cooling and liquid-cooling heat dissipation behaviors towards the battery control unit after being triggered; a phase change heat dissipation deployment component, used for being deployed at the battery control unit to absorb waste heat when the battery control unit reaches the phase change temperature; adjusting the heat dissipation strategy according to the real-time temperature and load, comprehensively evaluating the operating conditions of the battery, ensuring timely response to problems, and customizing the segmentation of the acquisition period can more flexibly manage the data cycle, making the trend analysis more accurate, facilitating subsequent analysis and comparison, and providing a rich data basis for deep learning.

Description

Technical Field

[0001] The present invention relates to the technical field of battery temperature control, and specifically provides a heat dissipation mechanism and 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 states at specific temperatures. During the charging and discharging process of the battery, heat is generated inside the battery. Phase change materials can absorb the excess heat when the temperature reaches their phase change points, thereby preventing the battery from overheating. For large-capacity mobile energy storage batteries, due to their stronger charging and discharging capabilities than ordinary energy storage batteries, they are more likely to generate heat during the charging and discharging process. If the heat dissipation is poor, the temperature rise may lead to 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 the changes in battery load in real time, lack flexibility in heat management, and are difficult to adjust according to the battery state and environmental changes in real time, resulting in insufficient or excessive heat dissipation. The application of phase change materials is relatively simple, lacking reasonable detection of their usage status, and failing to fully utilize their advantages in thermal management;

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

[0005] (I) Technical Problems to be Solved

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

[0007] (II) Technical Solutions

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

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

[0010] A battery control unit, as the main energy storage end, supports external devices for charging and discharging operations, carries various functional modules and functional units, and edits and issues control instructions;

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

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

[0013] A data detection unit, which is used to collect real-time ambient temperature data, power storage state data, and phase change state data around the battery control unit;

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

[0015] A trend integration module, which is used to customarily segment the acquisition period of the data detection unit, obtain all the collected data within a preset period, and generate several period trend data sets;

[0016] A threshold analysis module, which is used to compare whether there is an abnormality in the data within a certain period trend data set according to a preset threshold;

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

[0018] A parameter adjustment module, which is used to output the parameters for adjusting the function components, function modules, and function 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, a storage energy fluctuation detection module, and a phase change state detection module. The ambient temperature detection module, the storage energy fluctuation detection module, and the phase change state detection module are interconnected through a wireless network. The ambient temperature detection module is used to obtain the real-time temperature of the preset working area of the battery control unit. The storage energy fluctuation detection module is used to obtain the change state of the remaining stored electric energy under the charging and discharging states 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 with positive feedback by the storage module is as follows: when the parameter adjustment module submits the adjustment data, obtain the detection data response of the data detection unit in the next period, integrate it into a trend data set by the trend integration module, and then submit it to the threshold analysis module for threshold analysis. When there is no abnormal feedback in the trend data set, mark the adjustment data as positive feedback.

[0021] Further, the trend integration module is connected to the correlation analysis module through wireless network interaction. The correlation analysis module is connected to the storage module and the parameter adjustment module through wireless network interaction. The correlation analysis module is used to synchronously receive a certain period of trend data set from the trend integration module following the threshold analysis module, and obtain the data access permission of the storage module. Based on the preset correlation standard, it matches the historical trend data set that meets the correlation standard of the current period trend data set and its historical positive feedback adjustment parameters. This historical positive feedback adjustment data is submitted to the parameter adjustment module as an adjustment reference. When the state prediction module has no output response within the preset time period, the parameter adjustment module directly applies this adjustment data.

[0022] Further, the battery control unit is 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 electrical media interaction. The battery control unit is connected to the storage module through wireless network interaction. The trend integration module is connected to the data detection unit and the threshold analysis module through wireless network interaction. The state prediction module is connected to the threshold analysis module and the parameter adjustment module through wireless network interaction.

[0023] A heat dissipation method for a large-capacity mobile energy storage battery based on phase change heat dissipation technology, comprising 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 electrical energy change under charge and discharge states, and the state of the phase change materials in real time;

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

[0026] Step 3: Through custom time period segmentation, extract the data collected in each segmentation period and generate a trend data set, and 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 perform predictive analysis on the input abnormal period trend data, output the current abnormal expression data and the future abnormal trend, output the comprehensive abnormal data, and identify the fault factors;

[0028] Step 5: Generate adjustment setting parameters according to 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 adjustment;

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

[0030] Step 7: Store the collected data and the preset adjustment data, evaluate the adjustment data. If the evaluated positive feedback coefficient reaches the preset value, it will be marked as positive feedback adjustment data; otherwise, generate an adjustment strategy and submit it to the management terminal.

[0031] Furthermore, the abnormal trend analysis model is constructed through a deep learning algorithm. The original preset working area temperature, the electrical energy change under charge and discharge states, the state data of the phase change material, and the corresponding positive feedback adjustment data are obtained as training samples for training. Several statistical features are extracted and standardized. The collected abnormal period trend data is input into the trained abnormal trend analysis model. The abnormal trend analysis model outputs the current abnormal state. By defining a threshold, it is determined whether it is a fault, and the current abnormal expression and the trend prediction for the future period are output.

[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 decay of several fault coefficients is obtained. 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 decay factor of the fault coefficient.

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

[0036] (III) Beneficial effects

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

[0038] 1. By combining active air cooling and liquid cooling with phase change materials, it is possible to dynamically adjust the heat dissipation strategy according to real-time temperature and load, improve the heat dissipation efficiency, and monitor the ambient temperature, power state, and phase change state in real time, comprehensively evaluate the operating conditions of the battery, ensure timely response to problems, customize the segmentation of the acquisition period, manage the data cycle more flexibly, make the trend analysis more accurate, facilitate subsequent analysis and comparison, and provide a rich data basis for deep learning.

[0039] 2. An abnormal trend analysis model constructed by deep learning algorithms analyzes the input data during abnormal periods, integrates various fault factors, effectively detects abnormalities in the acquired data, forms comprehensive abnormal data alarms and countermeasures, thereby predicting potential system failures in advance, enhancing the early warning ability, providing guarantee for the stability of the system, and enabling the battery management system to have self-healing and self-optimization capabilities through fault adjustment feedback, thus avoiding the occurrence of major failures.

[0040] 3. By taking measures of correlation analysis with historical data, when fault data occurs, the available adjustment data of similar fault data in the repository can be immediately called as an emergency treatment plan, which can be quickly put into application in an urgent state, and can be automatically put into application when the model is in a long-term unresponsive state due to excessive computing power or other factors. Analyze the adjustment data, verify the detection results, and obtain available adjustment data as matching samples, so that in the long-term use process, the correlation analysis and the abnormal trend analysis model are also in a continuous learning process, thereby reducing the limitations in use. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

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

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

[0044] The reference numerals in the figure respectively represent: 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. Association analysis module. Detailed implementation manners

[0045] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] The following further describes the present invention with reference to embodiments.

[0047] Embodiment 1

[0048] A heat dissipation mechanism for a large-capacity mobile energy storage battery based on phase change heat dissipation technology in this embodiment, as Figure 1 shown, includes:

[0049] The battery control unit 1, as the main energy storage end, supports external devices to perform charging and discharging operations, 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 heat dissipation behaviors towards the battery control unit 1 after being triggered, 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 to absorb the waste heat when the battery control unit 1 reaches the phase change temperature, effectively utilizing the generated waste heat, improving the energy utilization efficiency, and extending the service life of the device;

[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 connected to the correlation analysis module 10 through wireless network interaction. The correlation analysis module 10 is connected to the storage module 5 and the parameter adjustment module 9 through wireless network interaction. The correlation analysis module 10 is used to synchronously receive the trend data set of a certain period from the trend integration module 6 following the threshold analysis module 7, and obtain the data access permission of the storage module 5. Based on the preset correlation standard, it matches the historical trend data set that meets the correlation standard of the current period trend data set and its historical positive feedback adjustment parameters. 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 applies this adjustment data. Using the preset correlation standard to match the historical trend data set can make more effective use of the existing data resources, realize the in-depth mining and value improvement of the data. Taking the historical positive feedback adjustment parameters as the adjustment reference can improve the scientificity and effectiveness of the adjustment decision to a certain extent, thereby improving the overall performance of the system. In the case where the state prediction module 8 fails to output a response within the preset time, the parameter adjustment module 9 can automatically make adjustments, reducing manual intervention, improving the self-adjustment ability and response speed of the system. By integrating the analysis of real-time data and historical data, it provides more comprehensive and accurate support for decision-making, helping relevant personnel make better decisions.

[0059] As an implementation method in this embodiment, as Figure 1 shown, the battery control unit 1 is connected to 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 electrical medium interaction. The battery control unit 1 is connected to the storage module 5 through wireless network interaction. The trend integration module 6 is connected to the data detection unit 4 and the threshold analysis module 7 through wireless network interaction. The state prediction module 8 is connected to the threshold analysis module 7 and the parameter adjustment module 9 through wireless network interaction.

[0060] In the specific implementation of this embodiment, the charge and discharge behavior and the control behavior of each module unit are carried out through the battery control unit 1. The battery control unit 1 is actively cooled by the active heat dissipation execution component 2. The phase change heat dissipation deployment component 3 is deployed to the battery control unit 1 to absorb 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 in the charge and discharge 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 the trend integration module 6 in different time periods. The threshold analysis module 7 determines whether there is an abnormality in the integrated data. If so, it is input to the parameter adjustment module 9, and the current and future abnormality predictions of the battery control unit 1 are output. The parameter adjustment module 9 makes a planning adjustment and submits it to the battery control unit 1. The correlation analysis module 10 then calls the historical adjustment data in the storage module 5 as a reference for adjustment, and directly applies the historical solution when the state prediction module 8 has no response;

[0061] Furthermore, this embodiment ensures that the system can respond to abnormal situations in a timely manner by real-time monitoring of the battery status and ambient temperature, uses deep learning technology for fault prediction, improves the intelligent efficiency of the system, responds to potential problems faster, improves the reliability of the adjustment data, enhances the self-learning ability of the system, and ensures the continuous optimization of the system performance.

[0062] Embodiment 2

[0063] On other levels, this embodiment also provides a heat dissipation method for a large-capacity mobile energy storage battery based on phase change heat dissipation technology, as Figure 2 shown, including the following steps:

[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 electrical energy change under the charge and discharge state, and the state of the phase change materials in real time;

[0065] Step 2: When the energy storage battery reaches the preset phase change temperature, the phase change materials absorb the excess heat and automatically trigger the heat dissipation mechanism, and the air cooling and liquid cooling measures are respectively enabled;

[0066] Step 3: Through custom time period segmentation, extract the data collected in each segmentation period and generate a trend data set, and 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 perform predictive analysis on the input abnormal period trend data, output the current abnormal expression data and future abnormal trends, output the comprehensive abnormal data, and identify the fault factors; the abnormal trend analysis model is constructed through deep learning algorithms, obtaining the original preset working area temperature, the electrical energy change under charge and discharge states, and the state data of the phase change material and the corresponding positive feedback adjustment data as training samples for training, extracting several statistical features for standardization, inputting the collected abnormal period trend data into the trained abnormal trend analysis model, and the abnormal trend analysis model outputs the current abnormal state, determining whether it is a fault by defining a threshold, and outputting the current abnormal expression and the trend prediction for the future period;

[0068] Step 5: Generate adjustment setting parameters according to 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 adjusted energy storage battery;

[0070] Step 7: Store the collected data and the 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, generate an adjustment strategy and submit it to the management terminal.

[0071] Compared with the prior art, by collecting real-time data, constructing a trend data set and an abnormal trend analysis model, the system can perform prediction and adjustment based on current and historical data, using deep learning algorithms to train the abnormal trend analysis model to automatically identify and predict abnormalities, which helps to timely judge the fault state, feedback the comprehensive abnormal data and adjustment parameters to the energy storage battery, and each functional component makes adjustments according to the evaluation results, thereby optimizing 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, forming a self-improving closed loop.

[0072] Embodiment 3

[0073] In this embodiment, an evaluation calculation method for adjustment data is provided, and the specific evaluation process is as follows: Based on the detection data of the next cycle, obtain the positive feedback coefficient based on the decay of several fault coefficients, and its calculation formula is:

[0074]

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

[0076] The working logic of the calculation formula is: for each detected data item, through (D i -T i ) calculate the difference between it and the target value. The target value is the ideal state that the system should reach during normal operation. 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] Introduce the fault coefficient to adjust the difference. The fault coefficient represents the degree of fault related to this detection index. Using the method of exponential decay, the higher the fault coefficient, the more serious the degree of fault and the greater the attenuation impact, indicating 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] Sum up all the adjusted differences to obtain the total contribution value. The weight for adjusting each difference is obtained by perform summation to obtain the normalization factor. Divide the accumulated total contribution value by the normalized factor to obtain the positive feedback coefficient. This coefficient synthesizes the effectiveness of all detected data and more truly reflects whether the submitted adjustment data has an actual positive impact on the system after weakening the fault impact

[0079] In summary, the present invention deploys phase change materials in the energy storage battery. When the battery temperature reaches the preset phase change temperature, the heat dissipation mechanism is automatically triggered, and the temperature, charge and discharge state of the energy storage battery and the state of the phase change material are collected in real time, which can dynamically monitor the battery performance and make timely responses, thereby ensuring the safe and stable operation of the battery. By extracting the data in each segmentation cycle and performing trend analysis, and matching the historical data based on the current trend set for positive feedback adjustment, the prediction and adjustment ability of the battery state is improved, enabling the battery to optimize its performance under different working conditions

[0080] The abnormal trend analysis model constructed by using deep learning algorithms can perform predictive analysis on the input abnormal data, identify fault factors, enhance the early warning ability for potential faults with intelligent analysis means, improve the self-healing ability of the system, perform corresponding functional adjustments on the energy storage battery according to the adjustment parameters generated from the comprehensive abnormal data, effectively avoid the occurrence of faults, improve the overall reliability of the system, continuously monitor the performance after adjustment and store data, and realize an intelligent adjustment strategy by combining the evaluation positive feedback coefficient, which not only optimizes the operating state of the battery, but also continuously improves the system performance, forming a virtuous cycle.

[0081] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A heat dissipation mechanism for a large-capacity mobile energy storage battery based on phase change heat dissipation technology, characterized in that Including: A battery control unit (1), as the main energy storage terminal, supports external devices for charging and discharging operations, carries various functional modules and functional units, and edits and issues control instructions; An active heat dissipation execution component (2), used to perform air-cooling and liquid-cooling heat dissipation behaviors towards the battery control unit (1) after being triggered; A phase change heat dissipation deployment component (3), used to be deployed at the battery control unit (1) to absorb residual heat when the battery control unit (1) reaches the phase change temperature; A data detection unit (4), used to collect real-time ambient temperature data, electrical energy storage state data, and phase change state data around the battery control unit (1); A storage module (5), used to store all collected data and analyzed data, and store the adjustment data marked as positive feedback and its associated historical trend data sets, supporting cloud backup; A trend integration module (6), used to customarily segment the collection period of the data detection unit (4), obtain all collected data within a preset period, and generate several period trend data sets; A threshold analysis module (7), used to compare whether there is an abnormality in the data within a certain period trend data set according to a preset threshold; A state prediction module (8), used to construct an abnormal trend analysis model through a deep learning algorithm, input the 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 a preset future period, and integrate them into comprehensive abnormal data; A parameter adjustment module (9), used to output the parameters for adjusting 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 (8), and send them to the battery control unit (1) for corresponding adjustment.

2. The heat dissipation mechanism of a large-capacity mobile energy storage battery based on the phase change heat dissipation technology according to claim 1, characterized in that 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). The ambient temperature detection module (41), the energy storage fluctuation detection module (42), and the phase change state detection module (43) are interconnected 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 change state of the remaining stored electrical energy under the charging and discharging states of the battery control unit (1), 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. A heat dissipation mechanism for a large-capacity mobile energy storage battery based on a phase change heat dissipation technology according to claim 1, 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, obtain the detection data response of the data detection unit (4) in the next period, integrate it into a trend data set by the trend integration module (6), and submit it to the threshold analysis module (7) for threshold analysis. When there is no abnormal feedback in the trend data set, mark the adjustment data as positive feedback.

4. A heat dissipation mechanism for a large-capacity mobile energy storage battery based on a phase change heat dissipation technology according to claim 1, characterized in that, The trend integration module (6) is connected to the correlation analysis module (10) through wireless network interaction. The correlation analysis module (10) is connected to the storage module (5) and the parameter adjustment module (9) through wireless network interaction. The correlation analysis module (10) is used to synchronously receive a certain period of trend data set from the trend integration module (6) following the threshold analysis module (7), and obtain the data access permission of the storage module (5). Based on the preset correlation standard, it matches the historical trend data set and its historical positive feedback adjustment parameters that meet the correlation standard of the current period trend data set. This 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 applies this adjustment data.

5. A heat dissipation mechanism for a large-capacity mobile energy storage battery based on phase change heat dissipation technology according to claim 1, characterized in that, The battery control unit (1) is connected to 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 electrical media interaction. The battery control unit (1) is connected to the storage module (5) through wireless network interaction. The trend integration module (6) is connected to the data detection unit (4) and the threshold analysis module (7) through wireless network interaction. The state prediction module (8) is connected to the threshold analysis module (7) and the parameter adjustment module (9) through wireless network interaction.

6. A heat dissipation method for a large-capacity mobile energy storage battery based on phase change heat dissipation technology, the method being an implementation method of a heat dissipation mechanism for a large-capacity mobile energy storage battery based on phase change heat dissipation technology according to any one of claims 1-5, characterized in that, Including the following steps: 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 electrical energy change under charge and discharge states, and the state of the phase change materials in real time. Step 2: When the energy storage battery reaches the preset phase change temperature, the phase change materials absorb the excess heat and automatically trigger the heat dissipation mechanism, and enable air cooling and liquid cooling measures respectively. Step 3: Through custom time period segmentation, extract the data collected in each segmentation period and generate a trend data set, and conduct 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 conduct predictive analysis on the input abnormal period trend data, output the current abnormal expression data and future abnormal trends, output comprehensive abnormal data, and identify the fault factors. Step 5: Generate adjustment setting parameters according to 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: Execute the corresponding adjustments according to the adjustment parameters generated in Step 5, and continuously monitor the performance of the adjusted energy storage battery. Step 7: Store the collected data and the preset adjustment data, evaluate the adjustment data. If the evaluated positive feedback coefficient reaches the preset value, it will be marked as positive feedback adjustment data. Otherwise, generate an adjustment strategy and submit it to the management end.

7. A heat dissipation method for a large-capacity mobile energy storage battery based on a phase change heat dissipation technology according to claim 6, characterized in that, The abnormal trend analysis model is constructed through a deep learning algorithm. The original preset working area temperature, the electrical energy change under charge and discharge states, and the state data of the phase change material and the corresponding positive feedback adjustment data are obtained as training samples for training. Several statistical features are extracted and standardized. The collected abnormal period trend data is input into the trained abnormal trend analysis model. The abnormal trend analysis model outputs the current abnormal state. By defining a threshold, it is determined whether it is a fault, and the current abnormal expression and the trend prediction for the future period are output.

8. A heat dissipation method for a large-capacity mobile energy storage battery based on a phase change heat dissipation technology according to claim 6, 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 decay of several fault coefficients is obtained, and its calculation formula is: Wherein, 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 decay factor of the fault coefficient.

9. A heat dissipation method for a large-capacity mobile energy storage battery 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 to adjust the difference, accumulate all the adjusted differences to obtain the total contribution value, sum the weights of each difference adjustment to obtain the normalization factor, and divide the accumulated total contribution value by the normalized factor to obtain the positive feedback coefficient.

Images