Charging pile intelligent heat dissipation method and device based on heat conduction line optimization and medium

Abstract

The invention discloses a charging pile intelligent heat dissipation method and device based on heat conduction line optimization and a medium, belongs to the technical field of charging pile heat dissipation, and aims to solve the technical problem of how to avoid the defects of high thermal resistance, response lag and large energy consumption in a traditional charging pile heat dissipation scheme and improve the heat dissipation efficiency and operation reliability of a charging pile. According to the technical scheme, a heat conduction structure is optimized; deploying a thin film thermocouple and constructing a three-dimensional temperature field model through infrared thermal imaging; establishing a thermal resistance-flow-power transfer function model by adopting a fuzzy PID (Proportion Integration Differentiation) and model predictive control hybrid algorithm; historical charging data are analyzed based on an LSTM neural network, a power peak value is predicted 300 ms ahead of time, and heat dissipation equipment is pre-started.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile heat dissipation, and specifically to an intelligent heat dissipation method, device and medium for charging piles based on optimized heat conduction lines. Background Art

[0002] Currently, the heat dissipation of charging piles mainly adopts air-cooled heat dissipation, liquid-cooled heat dissipation and phase change material heat dissipation. The specific solutions are as follows:

[0003] ① Air-cooled heat dissipation is through forced convection of heat sinks and fans, but the heat dissipation efficiency is low (heat flux density < 10 W / cm 2 ), and it cannot meet the high-power requirements.

[0004] ② Liquid-cooled heat dissipation is to adopt a coolant circulation system. Although it improves the heat dissipation ability, there are problems of complex pipelines and high maintenance costs.

[0005] ③ Phase change material heat dissipation is to utilize the heat absorption of phase change materials, but there are defects of slow heat response speed and poor cycle stability.

[0006] In summary, the existing heat dissipation has the following defects:

[0007] ① Redundant heat conduction path: The heat flow path of the traditional heat dissipation structure is fixed, resulting in more than 30% of the heat unable to be effectively exported.

[0008] ② Insufficient dynamic response: The existing temperature control system cannot match the charging power fluctuation in real time, resulting in local overheating (failure when the temperature rise rate > 5 °C / s).

[0009] ③ Low energy efficiency ratio: The energy consumption of the heat dissipation system accounts for 2.5% - 4% of the total power consumption of the charging pile, which does not meet the green energy standard.

[0010] Therefore, how to avoid the defects of high thermal resistance, lagging response and high energy consumption in the traditional charging pile heat dissipation scheme, and improve the heat dissipation efficiency and operation reliability of the charging pile is a technical problem to be solved urgently at present. Summary of the Invention

[0011] The technical task of the present invention is to provide an intelligent heat dissipation method, device and medium for charging piles based on optimized heat conduction lines to solve the problem of how to avoid the defects of high thermal resistance, lagging response and high energy consumption in the traditional charging pile heat dissipation scheme, and improve the heat dissipation efficiency and operation reliability of the charging pile.

[0012] The technical task of the present invention is achieved in the following way. An intelligent heat dissipation method for charging piles based on optimized heat conduction lines is as follows:

[0013] Optimize the heat conduction structure;

[0014] Deploy thin-film thermocouples and construct a three-dimensional temperature field model through infrared thermal imaging;

[0015] Adopt a hybrid algorithm of fuzzy PID and model predictive control (MPC) to establish a transfer function model of thermal resistance - flow rate - power, and the formula is as follows:

[0016]

[0017] Among them, the parameters are optimized online through model predictive control (MPC); k p represents a proportional coefficient; ΔT represents the temperature difference, that is, the difference between the current temperature and a certain reference temperature or target temperature; k i represents an integral coefficient; T(t) represents the temperature at time t; k d represents a differential coefficient; represents the rate of change of temperature with time, that is, the rate of change of temperature;

[0018] Analyze historical charging data based on the LSTM neural network, predict the power peak 300 ms in advance, and pre - start the heat dissipation device.

[0019] Preferably, the heat conduction structure is optimized as follows:

[0020] Adopt a multi - layer composite substrate and laser - etch micron - level grooves on the surface of the multi - layer composite substrate to form a directional heat conduction channel to achieve anisotropic heat conduction;

[0021] Adopt a dendritic fractal heat pipe network, that is, arrange several groups of bionic bifurcated heat pipes around the power device;

[0022] Fill nano - encapsulated phase - change materials between the multi - layer composite substrate and the bionic bifurcated heat pipes to form a gradient distribution layer with a thickness of 0.5 - 1 mm between the multi - layer composite substrate and the bionic bifurcated heat pipes.

[0023] Preferably, the multi - layer composite substrate adopts a copper - graphene - ceramic composite material, and the thickness of the multi - layer composite substrate is 2.5 mm, and the thermal conductivity is 650 W / m·K.

[0024] Preferably, the dendritic fractal heat pipe network is as follows:

[0025] Based on the bifurcated structure generated by the L - system algorithm (fractal dimension 1.8 - 2.2), the diameter of the main heat pipe is 6 mm, the secondary branch is 3 mm, the tertiary branch is 1.5 mm, and the heat flow path length is shortened by 40%.

[0026] Preferably, 8 groups of bionic bifurcated heat pipes are set, the diameter of a single bionic bifurcated heat pipe is 6 mm, and the thermal resistance is reduced to 0.15℃ / W.

[0027] Preferably, a thermal resistance dynamic equation is established by adopting a hybrid algorithm of fuzzy PID and model predictive control (MPC), and the formula is as follows:

[0028]

[0029] Among them, α and β represent real-time optimization parameters.

[0030] Preferably, the phase change material is paraffin wax wrapped with SiO2, and the phase change temperature is 60 - 80°C;

[0031] The heat storage density of the nano-encapsulated phase change material reaches 3.8 kJ / dm 3 .

[0032] More preferably, the control process during the charging of the charging pile is as follows:

[0033] Initial state: The phase change material is on standby, the fan runs at a low speed of 800 rpm;

[0034] Temperature ≥ 60°C: Start the phase change material to absorb heat, and the fan speed is increased to 1200 rpm;

[0035] Temperature ≥ 75°C: Turn on the liquid cooling pump with a flow rate of 5 L / min, and the fan runs at full speed of 2000 rpm;

[0036] Temperature ≥ 85°C: Trigger the power down protection.

[0037] An electronic device, comprising: a memory and at least one processor;

[0038] Among them, a computer program is stored on the memory;

[0039] The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the intelligent heat dissipation method for the charging pile based on the optimization of the heat conduction line as described above.

[0040] A computer-readable storage medium stores a computer program, and the computer program can be executed by a processor to implement the intelligent heat dissipation method for the charging pile based on the optimization of the heat conduction line as described above.

[0041] The intelligent heat dissipation method, device and medium for the charging pile based on the optimization of the heat conduction line of the present invention have the following advantages:

[0042] (1) By designing a multi-layer composite heat conduction structure, a dynamic heat flow distribution system and an intelligent temperature control algorithm, the present invention solves the technical defects of high thermal resistance, response lag and high energy consumption in the traditional heat dissipation scheme, and improves the heat dissipation efficiency and operation reliability of the charging pile;

[0043] (2) The heat dissipation efficiency of the present invention is increased by 40%, supporting a 4C fast charging rate, the device life is extended to 80,000 hours (MTBF), compatible with 400V / 800V charging platforms, and adapted to future technology upgrades;

[0044] (3) The multi-layer composite substrate of the present invention adopts a copper-graphene-ceramic sandwich structure (the copper layer conducts electricity, the graphene conducts heat horizontally, and the ceramic is insulated), and forms directional micro-grooves (width 50-100 μm) through laser micro-machining to achieve anisotropic heat conduction (longitudinal thermal conductivity ≥ 650 W / m·K, transverse ≤ 5 W / m·K);

[0045] (4) The dendritic fractal heat pipe network of the present invention is a bifurcated structure generated based on the L-system algorithm (fractal dimension 1.8-2.2), the main heat pipe has a diameter of 6 mm, the secondary branches have a diameter of 3 mm, and the tertiary branches have a diameter of 1.5 mm, shortening the heat flow path length by 40%;

[0046] (5) The gradient phase change energy storage layer of the present invention adopts a composite phase change material with paraffin wrapped by nano-SiO2 (the phase change temperature can be adjusted between 60-80 °C), and forms a gradient distribution layer with a thickness of 0.5-1 mm between the substrate and the heat pipe;

[0047] (6) The present invention realizes the matching of the coefficient of thermal expansion through ANSYS Workbench (the difference in CTE between the substrate and the power device ≤ 1.5 ppm / °C), reduces the thermal stress by 60%, and adopts electromagnetic-thermal co-design, that is, the IGBT layout and the heat pipe direction are orthogonally arranged, reducing the electromagnetic interference (EMI) by 12 dB;

[0048] (7) The present invention analyzes the historical charging data based on the LSTM neural network according to the historical power, ambient temperature, and device aging data, predicts the power peak 300 ms in advance (error rate < 5%) and pre-starts the heat dissipation device, and activates the liquid cooling system in advance when the predicted temperature change rate ≥ 3 °C / s;

[0049] (8) The heat flux density of the present invention is increased to more than 25 W / cm 2 Above, the heat response time is shortened to within 200 ms, and the heat dissipation energy consumption is reduced to less than 1.2% of the total power consumption. Description of the Drawings

[0050] The present invention will be further described below with reference to the drawings.

[0051] Appendix Figure 1 It is a schematic diagram of an intelligent heat dissipation method for a charging pile based on the optimization of the heat conduction line. Detailed Embodiments

[0052] The intelligent heat dissipation method, device and medium for a charging pile based on the optimization of the heat conduction line of the present invention will be described in detail below with reference to the schematic diagrams of the specification and specific embodiments.

[0053] Example 1:

[0054] As shown in the appendix Figure 1As shown in the figure, this embodiment provides an intelligent heat dissipation method for a charging pile based on the optimization of the heat conduction circuit, and the method is as follows:

[0055] S1. Optimize the heat conduction structure;

[0056] S2. Deploy thin-film thermocouples and construct a three-dimensional temperature field model through infrared thermal imaging;

[0057] S3. Adopt a hybrid algorithm of fuzzy PID and model predictive control (MPC) to establish a transfer function model of thermal resistance - flow - power, and the formula is as follows:

[0058]

[0059] Among them, the parameters are optimized online through model predictive control (MPC); k p represents a proportionality coefficient; ΔT represents the temperature difference, that is, the difference between the current temperature and a certain reference temperature or target temperature; k i represents an integral coefficient; T(t) represents the temperature at time t; k d represents a differential coefficient; represents the rate of change of temperature with time, that is, the change speed of temperature;

[0060] S4. Analyze historical charging data based on the LSTM neural network, predict the power peak 300 ms in advance, and pre-start the heat dissipation device.

[0061] The optimization of the heat conduction structure in step S1 of this embodiment is specifically as follows:

[0062] S101. Adopt a multi-layer composite substrate and laser-etch micron-scale grooves (width 50 - 100 μm) on the surface of the multi-layer composite substrate to form a directional heat conduction channel, realizing anisotropic heat conduction (longitudinal thermal conductivity ≥ 650 W / m·K, transverse ≤ 5 W / m·K);

[0063] S102. Adopt a dendritic fractal heat pipe network, that is, arrange several groups of bionic bifurcated heat pipes around the power device;

[0064] S103. Fill the nano-encapsulated phase change material between the multi-layer composite substrate and the bionic bifurcated heat pipe to form a gradient distribution layer with a thickness of 0.5 - 1 mm between the multi-layer composite substrate and the bionic bifurcated heat pipe.

[0065] The multi-layer composite substrate in this embodiment adopts a copper-graphene-ceramic composite material, and the thickness of the multi-layer composite substrate is 2.5 mm, and the thermal conductivity is 650 W / m·K.

[0066] The dendritic fractal heat pipe network in this embodiment is specifically as follows:

[0067] Fork structure generated based on the L-system algorithm (fractal dimension 1.8 - 2.2), with the main heat pipe having a diameter of 6 mm, secondary branches of 3 mm, and tertiary branches of 1.5 mm, and the heat flow path length shortened by 40%.

[0068] In this embodiment, 8 groups of bionic fork heat pipes are provided, with a single bionic fork heat pipe having a diameter of 6 mm and the thermal resistance reduced to 0.15 °C / W.

[0069] In this embodiment, 16 thin-film thermocouples (accuracy ±0.5 °C) and 3 infrared thermal imaging units are deployed to construct a three-dimensional temperature field model.

[0070] In step S3 of this embodiment, a hybrid algorithm of fuzzy PID and model predictive control (MPC) is used to establish a dynamic thermal resistance equation, as follows:

[0071]

[0072] Among them, α and β represent real-time optimization parameters.

[0073] In this embodiment, the phase change material uses paraffin wrapped in SiO2, with a phase change temperature of 60 - 80 °C.

[0074] In this embodiment, the heat storage density of the nano-encapsulated phase change material reaches 3.8 kJ / dm 3 .

[0075] Application in a 120 kW DC charging pile is as follows:

[0076] Structural parameters are as follows:

[0077] ① Substrate size: 300 mm × 200 mm × 2.5 mm;

[0078] ② Heat pipe layout: 8 groups of main heat pipes, each group having 3 levels of branches;

[0079] ③ Phase change material filling amount: 1.2 L.

[0080] The control process during the charging of the charging pile in this embodiment is as follows:

[0081] Initial state: The phase change material is on standby, the fan runs at a low speed of 800 rpm;

[0082] When the temperature ≥ 60 °C: Start the phase change material to absorb heat, and the fan speed is increased to 1200 rpm;

[0083] When the temperature ≥ 75 °C: Turn on the liquid cooling pump, with a flow rate of 5 L / min, and the fan runs at full speed of 2000 rpm;

[0084] When the temperature ≥ 85 °C: Trigger the power reduction protection.

[0085] The extended application of the 240kW ultra-fast charging pile is as follows:

[0086] ① Adopt modular design and parallel two groups of heat dissipation units;

[0087] ② Add a magnetohydrodynamic drive pump (flow rate 8L / min);

[0088] ③ The measured heat flux density reaches 26.5W / cm 2 .

[0089] Example 2:

[0090] This embodiment also provides an electronic device, including: a memory and a processor;

[0091] Wherein, the memory stores computer execution instructions;

[0092] The processor executes the computer execution instructions stored in the memory, so that the processor executes the intelligent heat dissipation method of the charging pile based on the optimization of the heat conduction line in any embodiment of the present invention.

[0093] The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0094] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory may also include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, at least one magnetic disk storage period, a flash memory device, or other volatile solid-state storage devices.

[0095] Example 3:

[0096] This embodiment also provides a computer-readable storage medium storing multiple instructions that are loaded by a processor to cause the processor to execute the intelligent charging pile heat dissipation method based on heat conduction line optimization according to any embodiment of the present invention. Specifically, a system or device equipped with the storage medium can be provided, and software program code for implementing the functions of any one of the above embodiments is stored on the storage medium, and the computer (or CPU or MPU) of the system or device reads and executes the program code stored on the storage medium.

[0097] In this case, the program code read from the storage medium itself can implement the functions of any one of the above embodiments, so the program code and the storage medium storing the program code constitute a part of the present invention.

[0098] Examples of the storage medium for providing the program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer via a communication network.

[0099] Furthermore, it should be clear that not only can the functions of any one of the above embodiments be implemented by executing the program code read by the computer, but also by the operating system or the like operating on the computer based on the instructions of the program code to complete part or all of the actual operations.

[0100] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or the memory provided in the expansion unit connected to the computer, and then the CPU or the like installed on the expansion board or the expansion unit executes part and all of the actual operations based on the instructions of the program code, so as to implement the functions of any one of the above embodiments.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended 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 for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent heat dissipation method for a charging pile based on heat conduction circuit optimization, characterized in that: The method is as follows: Optimize heat conduction structure; Deploy thin-film thermocouples and use infrared thermal imaging to build a three-dimensional temperature field model; A hybrid algorithm of fuzzy PID and model predictive control is used to establish a transfer function model of thermal resistance-flow-power. The formula is as follows: Among them, the parameters are optimized online through model predictive control; k p represents a proportionality coefficient; ΔT represents the temperature difference, that is, the difference between the current temperature and a reference temperature or target temperature; k i represents an integral coefficient; T(t) represents the temperature at time t; k d represents a differential coefficient; It indicates the rate of change of temperature over time, that is, the speed of temperature change; Based on the LSTM neural network, historical charging data is analyzed to predict the power peak 300ms in advance and pre-start the cooling equipment.

2. The intelligent heat dissipation method for charging piles based on heat conduction line optimization according to claim 1 is characterized in that: The optimized heat conduction structure is as follows: A multi-layer composite substrate is used and micron-scale grooves are laser-etched on the surface of the multi-layer composite substrate to form a directional heat conduction channel to achieve anisotropic heat conduction; A tree-like fractal heat pipe network is used, that is, several groups of bionic bifurcated heat pipes are arranged around the power device; Nano-wrapped phase change material is filled between the multi-layer composite substrate and the bionic bifurcated heat pipe to form a gradient distribution layer with a thickness of 0.5-1 mm between the multi-layer composite substrate and the bionic bifurcated heat pipe.

3. The intelligent heat dissipation method for charging piles based on heat conduction line optimization according to claim 1 is characterized in that: The multi-layer composite substrate adopts a copper-graphene-ceramic composite material and the thickness of the multi-layer composite substrate is 2.5 mm, and the thermal conductivity is 650 W / m·K.

4. The intelligent heat dissipation method for charging piles based on heat conduction line optimization according to claim 1 is characterized in that: The tree-like fractal heat pipe network is as follows: Based on the bifurcated structure generated by the L-system algorithm, the main heat pipe has a diameter of 6mm, the secondary branch is 3mm, and the tertiary branch is 1.5mm, and the heat flow path length is shortened by 40%.

5. The intelligent heat dissipation method for charging piles based on heat conduction line optimization according to claim 1 is characterized in that: There are 8 groups of bionic bifurcated heat pipes, the diameter of a single bionic bifurcated heat pipe is 6mm, and the thermal resistance is reduced to 0.15℃ / W.

6. The intelligent heat dissipation method for charging piles based on heat conduction line optimization according to claim 1 is characterized in that: The thermal resistance dynamic equation is established by using the hybrid algorithm of fuzzy PID and model predictive control. The formula is as follows: Among them, α and β represent real-time optimization parameters.

7. The intelligent heat dissipation method for charging piles based on heat conduction line optimization according to claim 1 is characterized in that: The phase change material uses SiO2 wrapped in paraffin, and the phase change temperature is 60-80℃; The heat storage density of nano-encapsulated phase change materials reaches 3.8 kJ / dm 3 .

8. The intelligent heat dissipation method for charging piles based on heat conduction line optimization according to any one of claims 1 to 7, characterized in that: The specific control process of the charging pile during charging is as follows: Initial state: phase change material is on standby, fan is running at low speed, 800rpm; Temperature ≥60℃: The phase change material is activated to absorb heat and the fan speed is increased to 1200rpm; Temperature ≥75℃: Turn on the liquid cooling pump, flow rate 5L / min, fan full speed to 2000rpm; Temperature ≥85℃: trigger power reduction protection.

9. An electronic device, characterized in that: include: memory and at least one processor; Wherein, the memory stores a computer program; The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the intelligent heat dissipation method for charging piles based on heat conduction line optimization as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which can be executed by a processor to implement the intelligent heat dissipation method for charging piles based on heat conduction line optimization as described in any one of claims 1 to 8.

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