Graphene-based RBF-controlled battery cooling system

Abstract

The present application relates to the field of battery cooling. Disclosed is a graphene-based radial basis function (RBF)-controlled battery cooling system. The graphene-based RBF-controlled battery cooling system comprises: a temperature and pressure detection module, a temperature and pressure collection module, an RBF neutral network precision control module, a cooling module, an emergency alarm control module and a data display module. An RBF neutral network is used to precisely control battery heating and cooling processes; data of a battery and a graphene film on the surface of the battery is simultaneously collected, and the flow rate and temperature of a coolant in the cooling module are regulated by means of the collected data, such that the battery always operates in an optimal state; and data collected by the temperature and pressure collection module is compared with early warning values in the emergency alarm control module in real time, so that it can be ensured that the battery always operates in an optimal state, and the ideal operating voltage and temperature of the battery can be ensured, thereby improving the service life and safety performance of batteries.

Description

Battery cooling system based on graphene RBF control Technical Field

[0001] The present application relates to the field of battery cooling, and in particular to a battery cooling system based on graphene RBF control. Background Art

[0002] The battery cooling system is used to cool the battery pack in electric vehicles. It typically consists of components such as a radiator, heat pipes, a water pump, a water tank, and sensors. In high-temperature environments, the battery pack can overheat, affecting its performance and lifespan. To prevent this, the battery cooling system uses water or air cooling to keep the battery pack at a low temperature, ensuring its normal operation.

[0003] Among them, the water cooling system is a relatively common cooling method. It sends coolant into the battery pack to cool it quickly, thereby effectively reducing the temperature of the battery pack. At the same time, a graphene film is added to the battery to improve the battery's heat dissipation performance.

[0004] In traditional control systems, the use of coolant and graphene film for heat dissipation and cooling only continuously cools the overall temperature of the battery. It is difficult to control the battery to maintain the optimal temperature during heat dissipation and cooling, and maintain the optimal operating temperature of the battery. Summary of the Invention

[0005] The purpose of this application is to solve the problem that the use of coolant and graphene film for heat dissipation and cooling proposed in the above background technology only continuously cools down the overall temperature of the battery, and it is difficult to control the battery to maintain the battery temperature at the optimal temperature during heat dissipation and cooling, and maintain the optimal operating temperature of the battery. This application provides a battery cooling system based on graphene RBF control.

[0006] In order to achieve the above-mentioned purpose, this application specifically adopts the following technical solutions:

[0007] A battery cooling system based on graphene RBF control, the battery cooling system based on graphene RBF control comprising:

[0008] Temperature and pressure detection module, used to detect the battery surface temperature and the pressure of the battery charging and discharging surface;

[0009] Temperature and pressure collection module, used to collect battery surface temperature and pressure data;

[0010] The radial basis function (RBF) neural network precision control module uses a radial basis function (RBF) neural network to precisely control the heating and cooling process of the battery. By collecting data from the battery and graphene membrane, it automatically adjusts the flow rate and temperature of the coolant.

[0011] The cooling module uses graphene film as an efficient heat dissipation material and uses a specific coolant (such as HFE-7000) on the graphene surface for immersion cooling;

[0012] The emergency alarm control module compares the temperature and pressure collected by the temperature and pressure collection module with the pre-set warning values;

[0013] The data display module uses the display screen to display the temperature and pressure data collected by the temperature and pressure collection module in real time on the display screen.

[0014] Furthermore, the temperature and pressure collection module includes a data storage module, which stores temperature changes in real time, updates the change curve in time, and determines the health of the battery.

[0015] Furthermore, the temperature and pressure collection module includes a data comparison module, which pre-uploads the temperature and pressure of the normal operation of the battery and then compares them with the real-time data in the data storage module in a timely manner.

[0016] Furthermore, the radial basis function (RBF) neural network precision control module includes a real-time control module, and the real-time control module quickly responds to control requirements to achieve real-time control.

[0017] Furthermore, the radial basis function (RBF) neural network precision control module includes a prediction and compensation module, which uses the network to predict the future state of the system and makes corresponding control decisions to compensate for disturbances and uncertainties.

[0018] Furthermore, the radial basis function (RBF) neural network precision control module includes an adaptive module, which is used to implement an adaptive control strategy and can be adjusted online to adapt to changes in system parameters or external interference.

[0019] Furthermore, the emergency alarm control module includes a sound and light alarm module, and the emergency alarm control module includes an emergency power-off module.

[0020] In summary, the present application includes at least one of the following beneficial effects:

[0021] 1. This application utilizes the detection head in the temperature and pressure detection module to measure the temperature and pressure values ​​of the battery surface during charging and discharging, and then the temperature and pressure collection module collects the detected battery surface temperature and pressure values, and then uses the radial basis function (RBF) neural network to accurately control the heating and cooling process of the battery, while collecting data on the battery and the graphene film on the battery surface, and then uses the real-time control module to automatically adjust the flow rate and temperature of the coolant to ensure that the battery always operates in the best condition, and uses the data collected by the temperature and pressure collection module to compare with the warning value in the emergency alarm control module in real time. When the collected data value is higher than the warning value, the emergency alarm control module promptly issues an alarm, thereby ensuring that the battery always operates in the best condition, ensuring the ideal operating voltage and temperature of the battery, and improving the battery's service life and safety performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] FIG1 is a flow chart of the system in this application.

[0023] Description of reference numerals:

[0024] 1. Temperature and pressure detection module; 2. Temperature and pressure collection module; 3. Cooling module; 4. Radial basis function (RBF) neural network precision control module; 5. Emergency alarm control module; 6. Data display module; 7. Data storage module; 8. Data comparison module; 9. Real-time control module; 10. Prediction and compensation module; 11. Adaptive module; 12. Sound and light alarm module; 13. Emergency power-off module. DETAILED DESCRIPTION

[0025] The present application is further described in detail below with reference to FIG1 .

[0026] The embodiments of the present application disclose a battery cooling system based on graphene RBF control.

[0027] Referring to Figure 1, a battery cooling system based on graphene RBF control includes:

[0028] Temperature and pressure detection module 1, used to detect the battery surface temperature and the pressure of the battery charging and discharging surface;

[0029] Temperature and pressure collection module 2, used to collect battery surface temperature and pressure data;

[0030] Radial Basis Function (RBF) Neural Network Precision Control Module 4 uses a radial basis function (RBF) neural network to precisely control the heating and cooling process of the battery. By collecting data from the battery and graphene membrane, it automatically adjusts the flow rate and temperature of the coolant;

[0031] Cooling module 3 uses graphene film as a highly efficient heat dissipation material and uses a specific coolant (such as HFE-7000) on the graphene surface for immersion cooling;

[0032] The emergency alarm control module 5 compares the temperature and pressure collected by the temperature and pressure collection module 2 with the pre-set warning values;

[0033] The data display module 6 uses a display screen to display the temperature and pressure data collected by the temperature and pressure collection module 2 in real time on the display screen.

[0034] First, in the battery cooling system, the detection head in the temperature and pressure detection module 1 is used to measure the temperature and pressure values ​​of the battery surface during charging and discharging. The temperature and pressure collection module 2 then collects the detected battery surface temperature and pressure values. A radial basis function (RBF) neural network is then used to precisely control the battery heating and cooling process. Data of the battery and the graphene film on the battery surface are simultaneously collected. The flow rate and temperature of the coolant in the cooling module 3 are then adjusted based on the collected data to ensure that the battery always operates in an optimal state. The data collected by the temperature and pressure collection module 2 is compared in real time with the warning value in the emergency alarm control module 5. When the collected data value is higher than the warning value, the emergency alarm control module 5 promptly issues an alarm. At the same time, the data collected by the temperature and pressure collection module 2 is promptly displayed on the data display module 6. By using a radial basis function (RBF) neural network to precisely control the battery heating and cooling process, and by collecting data from the battery and the graphene film, the flow rate and temperature of the coolant are automatically adjusted, thereby ensuring that the battery always operates in an optimal state, ensuring the ideal operating voltage and temperature of the battery, and improving the battery's service life and safety performance.

[0035] In some embodiments, the temperature and pressure collection module 2 includes a data storage module 7 , which stores temperature changes in real time, updates the change curve in a timely manner, and determines the health of the battery.

[0036] By using the data storage module 7 to store the collected data in real time when using the temperature and pressure collection module 2 to collect data, it is possible to conveniently know the changes in temperature and pressure when the battery is charging and discharging.

[0037] In some embodiments, the temperature and pressure collection module 2 includes a data comparison module 8 , which pre-uploads the temperature and pressure of the normal operation of the battery and then compares them with the real-time data in the data storage module 7 in a timely manner.

[0038] By using the data comparison module 8 to perform real-time comparison on the data stored in the data storage module 7 , it is possible to quickly and conveniently learn the temperature and pressure change curves when the battery is operating normally.

[0039] In some embodiments, the radial basis function (RBF) neural network precision control module 4 includes a real-time control module 9 , which quickly responds to control requirements and implements real-time control.

[0040] By using a radial basis function (RBF) neural network to accurately control the heating and cooling process of the battery, by collecting data from the battery and graphene membrane, and then using the real-time control module 9 to automatically adjust the flow rate and temperature of the coolant, it is possible to ensure that the battery always operates in the best condition, ensure the ideal operating voltage and temperature of the battery, and improve the battery's service life and safety performance.

[0041] In some embodiments, the radial basis function (RBF) neural network precision control module 4 includes a prediction and compensation module 10 that uses the network to predict the future state of the system and makes corresponding control decisions to compensate for disturbances and uncertainties.

[0042] By using the prediction and compensation module 10 to use the network to predict the future temperature and pressure of the battery when it is working, and adjusting the control strategy in time, it is possible to conveniently and timely control the charging and discharging of the battery and reduce the interference of external uncertain factors.

[0043] In some embodiments, the radial basis function (RBF) neural network precision control module 4 includes an adaptive module 11 , which is used to implement an adaptive control strategy and can be adjusted online to adapt to changes in system parameters or external interference.

[0044] By utilizing the adaptive module 11 to timely control and adjust the collected data and provide timely feedback, the system can adapt to changes in system parameters and external interference.

[0045] In some embodiments, the emergency alarm control module 5 includes a sound and light alarm module 12 , and the emergency alarm control module 5 includes an emergency power-off module 13 .

[0046] By comparing the real-time values ​​of the normal operation of the battery using the comparison module, when the values ​​have a large deviation, the sound and light alarm module 12 is used to alarm, and the emergency power-off module 13 is used to cut off the power in time, so that the battery problems can be discovered in time during the use of the battery.

Claims

1. The battery cooling system based on graphene RBF control is characterized by: The battery cooling system based on graphene RBF control includes: A temperature and pressure detection module (1) is used to detect the battery surface temperature and the pressure of the battery charging and discharging surface; A temperature and pressure collection module (2) is used to collect data on battery surface temperature and pressure; A radial basis function (RBF) neural network precision control module (4) uses a radial basis function (RBF) neural network to precisely control the heating and cooling process of the battery, and automatically adjusts the flow rate and temperature of the coolant by collecting data from the battery and the graphene membrane; A cooling module (3) uses the graphene film as a high-efficiency heat dissipation material and uses a specific coolant (such as HFE-7000) on the graphene surface for immersion cooling; The emergency alarm control module (5) compares the temperature and pressure collected by the temperature and pressure collection module (2) with the pre-set warning values; The data display module (6) uses a display screen to display the temperature and pressure data collected by the temperature and pressure collection module (2) on the display screen in real time.

2. The battery cooling system based on graphene RBF control according to claim 1, characterized in that: The temperature and pressure collection module (2) includes a data storage module (7), and the data storage module (7) stores temperature changes in real time, updates the change curve in time, and determines the health of the battery.

3. The battery cooling system based on graphene RBF control according to claim 2, characterized in that: The temperature and pressure collection module (2) includes a data comparison module (8), which pre-uploads the temperature and pressure of the normal operation of the battery and then compares them with the real-time data in the data storage module (7).

4. The battery cooling system based on graphene RBF control according to claim 3 is characterized in that: The radial basis function (RBF) neural network precision control module (4) includes a real-time control module (9), and the real-time control module (9) quickly responds to control requirements to achieve real-time control.

5. The battery cooling system based on graphene RBF control according to claim 4 is characterized in that: The radial basis function (RBF) neural network precision control module (4) includes a prediction and compensation module (10), which uses the network to predict the future state of the system and makes corresponding control decisions to compensate for disturbances and uncertainties.

6. The battery cooling system based on graphene RBF control according to claim 5, characterized in that: The radial basis function (RBF) neural network precision control module (4) includes an adaptive module (11), which is used to implement an adaptive control strategy and can be adjusted online to adapt to changes in system parameters or external interference.

7. The battery cooling system based on graphene RBF control according to claim 1, characterized in that: The emergency alarm control module (5) includes a sound and light alarm module (12), and the emergency alarm control module (5) includes an emergency power-off module (13).

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