Parameter optimization method and device for direct current charging terminal of electric vehicle and test equipment

Abstract

The invention discloses a parameter optimization method and device for a direct current charging terminal of an electric vehicle and test equipment, and relates to the technical field of vehicle charging test. The method comprises the following steps: acquiring a current design parameter of a direct current charging terminal to be tested, simulating a temperature rise process of a terminal lap joint part under the condition of the current design parameter through a finite element model, and acquiring a maximum temperature rise value corresponding to the current design parameter; if the maximum temperature rise value corresponding to the current design parameter is smaller than a preset temperature rise limit value, the reliability corresponding to the current design parameter is calculated, and if the reliability corresponding to the current design parameter is larger than or equal to a preset reliability limit value, the current design parameter is obtained to serve as different design parameters in a limited neighborhood of an original point to be subjected to combined iteration, the maximum temperature rise value corresponding to the combined design parameters is larger than or equal to the preset temperature rise limit value. By adopting the method provided by the invention, the performance of the charging terminal can be improved, a basis is provided for subsequent revision of an automobile charging standard, and the charging safety is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle charging test, and in particular to a parameter optimization method, device and test equipment for a DC charging terminal of an electric vehicle. Background Art

[0002] As people's living standards continue to improve, cars have become an indispensable means of transportation, bringing great convenience to daily travel. At the same time, more and more gas emissions will cause environmental pollution. Therefore, under the background of green environmental protection initiatives, electric vehicles for various purposes have emerged.

[0003] During the DC charging process of electric vehicles, since both the charging gun plug and the charging socket have resistance, the thermal effect of the current will cause heat to be generated between the two, causing the temperature at the vehicle charging terminal connector to rise. In severe cases, it may cause fires and other safety accidents. There is no effective solution to this problem in the relevant technology. Summary of the invention

[0004] In view of the above-mentioned defects or deficiencies in the relevant technology, it is desired to provide a parameter optimization method, device and testing equipment for the DC charging terminal of an electric vehicle, which can improve the performance of the charging terminal, provide a basis for the subsequent revision of the automobile charging standards, and ensure charging safety.

[0005] In a first aspect, the present application provides a method for optimizing parameters of a DC charging terminal of an electric vehicle, the method comprising: Acquire current design parameters of the DC charging terminal to be tested, and simulate the temperature rise process of the terminal overlap part under the current design parameter conditions through a finite element model to obtain the maximum temperature rise value corresponding to the current design parameters, wherein the current design parameters include terminal length, terminal width, terminal thickness, terminal overlap part resistance, and terminal loading current; Comparing the maximum temperature rise value corresponding to the current design parameter with the preset temperature rise limit value, and when the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, calculating the reliability corresponding to the current design parameter; Compare the reliability corresponding to the current design parameter with the preset reliability limit value, and when the reliability corresponding to the current design parameter is greater than or equal to the preset reliability limit value, obtain different design parameters in a finite neighborhood with the current design parameter as the origin for combined iteration until the maximum temperature rise value corresponding to the combined design parameter is greater than or equal to the preset temperature rise limit value.

[0006] Optionally, in some embodiments of the present application, during the temperature rise process of the terminal overlap portion under the current design parameter conditions simulated by the finite element model, the current density of the terminal overlap portion satisfies the following formula: + =0; in, Indicates the conductivity of the terminal joint. Indicates any point of the terminal overlap ( )’s potential; The absorbed heat of the terminal overlap portion satisfies the following formula: Q = ; in, Indicates the heat generated by the internal heat source of the terminal. = , Indicates the terminal overlap position along The electric field strength in the direction Indicates the terminal overlap position along The electric field strength in the direction Indicates the terminal overlap position along The electric field strength in the direction; Indicates the heat dissipated from the terminal to the surrounding medium. =acon* ( ), acon represents the convection heat dissipation coefficient, the unit is W / (m 2 ℃), Indicates the convection heat dissipation area, in m 2 , Indicates the temperature of the heating element in °C. Indicates the ambient temperature in °C; Indicates the amount of heat dissipated by the terminal through thermal radiation. , represents the emissivity, which is 0.92. represents the Stefan-Boltzmann constant, which is 5.67×10 -8 W / (m 2 ·K 4 ), Indicates the radiation surface The area of Indicates that the radiation surface To the radiating surface The shape coefficient is taken as 1; The temperature field distribution of the terminal overlap portion satisfies the following formula: Q ( )= , ( ) ; in, Indicates any point of the terminal overlap ( )’s thermodynamic temperature, Indicates the terminal overlap position along The thermal conductivity in the direction, Indicates the terminal overlap position along The thermal conductivity in the direction, Indicates the terminal overlap position along The thermal conductivity in the direction, Q ( ) indicates any point of the terminal overlap ( ) absorbs heat, Indicates the material density of the terminal overlap. Indicates the specific heat capacity of the material at the terminal joint. Indicates time, Indicates field.

[0007] Optionally, in some embodiments of the present application, when the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, calculating the reliability corresponding to the current design parameter includes: A pre-trained first neural network model is used to perform function fitting on the current design parameters and the maximum temperature rise values ​​corresponding to the current design parameters, and the reliability corresponding to the current design parameters is obtained by Monte Carlo calculation.

[0008] Optionally, in some embodiments of the present application, the first neural network model includes 5 input layer neurons, 12 hidden layer neurons and 1 output layer neuron, the activation function of the hidden layer neurons in the first neural network model is the Sigmoid function, and the activation function of the output layer neurons in the first neural network model is the Purelin function.

[0009] Optionally, the method in some embodiments of the present application further includes obtaining the reliability and manufacturing cost corresponding to each of the combined design parameters if there are multiple combined design parameters whose corresponding maximum temperature rise values ​​are greater than or equal to the preset temperature rise limit value, and the reliability corresponding to the combined design parameters is obtained by performing function fitting on the combined design parameters and the maximum temperature rise values ​​corresponding to the combined design parameters using a pre-trained second neural network model, and is calculated by a Monte Carlo method; According to the reliability corresponding to the combined design parameters and the manufacturing cost, a final design parameter is selected from the plurality of combined design parameters.

[0010] Optionally, in some embodiments of the present application, the second neural network model includes 5 input layer neurons, 9 hidden layer neurons and 1 output layer neuron, and the activation function of the hidden layer neurons in the second neural network model and the activation function of the output layer neurons in the second neural network model are both Sigmoid functions.

[0011] In a second aspect, the present application provides a parameter optimization device for a DC charging terminal of an electric vehicle, the device comprising a simulation module, a first comparison module, and a second comparison module connected in sequence; The simulation module is configured to obtain current design parameters of the DC charging terminal to be tested, and simulate the temperature rise process of the terminal overlap part under the current design parameter conditions through a finite element model to obtain the maximum temperature rise value corresponding to the current design parameters, wherein the current design parameters include terminal length, terminal width, terminal thickness, terminal overlap part resistance, and terminal loading current; The first comparison module is configured to compare the maximum temperature rise value corresponding to the current design parameter with the preset temperature rise limit value, and when the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, calculate the reliability corresponding to the current design parameter; Furthermore, the second comparison module is configured to compare the reliability corresponding to the current design parameter with a preset reliability limit value, and when the reliability corresponding to the current design parameter is greater than or equal to the preset reliability limit value, obtain different design parameters within a finite neighborhood with the current design parameter as the origin for combined iteration until the maximum temperature rise value corresponding to the combined design parameter is greater than or equal to the preset temperature rise limit value.

[0012] Optionally, in some embodiments of the present application, the first comparison module is specifically used to use a pre-trained first neural network model to perform function fitting on the current design parameters and the maximum temperature rise values ​​corresponding to the current design parameters, and to obtain the reliability corresponding to the current design parameters through Monte Carlo calculation.

[0013] Optionally, the device in some embodiments of the present application further includes an acquisition module and a screening module connected in sequence; The acquisition module is configured to acquire the reliability and manufacturing cost corresponding to each of the combined design parameters if the maximum temperature rise values ​​corresponding to multiple combined design parameters are greater than or equal to the preset temperature rise limit value, and the reliability corresponding to the combined design parameters is obtained by performing function fitting on the combined design parameters and the maximum temperature rise values ​​corresponding to the combined design parameters using a pre-trained second neural network model, and is calculated by a Monte Carlo method; Furthermore, the screening module is configured to screen out final design parameters from the multiple combined design parameters according to the reliability corresponding to the combined design parameters and the manufacturing cost.

[0014] In a third aspect, the present application provides a testing device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the steps of any one of the methods described in the first aspect.

[0015] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages: The embodiment of the present application provides a parameter optimization method, device and test equipment for a DC charging terminal of an electric vehicle. The method uses a finite element model to safely and efficiently simulate the temperature rise process of the terminal overlap portion of the DC charging terminal to be tested under the current design parameter conditions, obtain the maximum temperature rise value corresponding to the current design parameter, and then compare the maximum temperature rise value corresponding to the current design parameter with the preset temperature rise limit value. If the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, it indicates that the performance of the DC charging terminal to be tested does not meet the requirements. Then, the reliability corresponding to the current design parameter is further compared with the preset reliability limit value. When the reliability corresponding to the current design parameter is greater than or equal to the preset reliability limit value, it indicates that the design parameter can be optimized. At this time, different design parameters in a finite neighborhood with the current design parameter as the origin are combined and iterated until the maximum temperature rise value corresponding to the combined design parameter is greater than or equal to the preset temperature rise limit value, thereby improving the performance of the charging terminal. This can provide a basis for the subsequent revision of the automobile charging standard and ensure charging safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic flow chart of a parameter optimization method for a DC charging terminal of an electric vehicle provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a first neural network model provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a second neural network model provided in an embodiment of the present application; Figure 4A structural block diagram of a parameter optimization device for a DC charging terminal of an electric vehicle provided in an embodiment of the present application; Figure 5 A structural block diagram of another device for optimizing parameters of a DC charging terminal of an electric vehicle provided in an embodiment of the present application; Figure 6 A structural block diagram of a test device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0020] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. Figures 1 to 6 The parameter optimization method, device and testing equipment of the DC charging terminal of an electric vehicle provided in the embodiments of the present application are described in detail.

[0021] Please refer to Figure 1 , which is a flow chart of a parameter optimization method for a DC charging terminal of an electric vehicle provided in an embodiment of the present application, and the parameter optimization method specifically comprises the following steps: S101, obtaining current design parameters of the DC charging terminal to be tested, and simulating the temperature rise process of the terminal overlap part under the current design parameter condition through a finite element model to obtain the maximum temperature rise value corresponding to the current design parameter.

[0022] It should be noted that the current design parameters in the embodiment of the present application include but are not limited to the terminal length X 1 、Terminal width X 2 、Terminal thickness X 3 , Terminal overlap resistance X 4 And the terminal load current X 5 wait.

[0023] For example, in the process of simulating the temperature rise of the terminal overlap portion under the current design parameter conditions by the finite element model, the current density of the terminal overlap portion satisfies the following formula, that is: + =0 (1) In formula (1), Indicates the conductivity of the terminal joint. Indicates any point of the terminal overlap ( )’s potential; The absorbed heat at the terminal overlap part satisfies the following formula, namely: Q = (2) In formula (2), Indicates the heat generated by the internal heat source of the terminal. = , Indicates the terminal overlap position along The electric field strength in the direction Indicates the terminal overlap position along The electric field strength in the direction Indicates the terminal overlap position along The electric field strength in the direction; Indicates the heat dissipated from the terminal to the surrounding medium. =acon* ( ), acon represents the convection heat dissipation coefficient, the unit is W / (m 2 ℃), Indicates the convection heat dissipation area, in m 2 , Indicates the temperature of the heating element in °C. Indicates the ambient temperature in °C; Indicates the amount of heat dissipated by the terminal through thermal radiation. , represents the emissivity, which is 0.92. represents the Stefan-Boltzmann constant, which is 5.67×10 -8 W / (m 2 ·K 4 ), Indicates the radiation surface The area of Indicates that the radiation surface To the radiating surface The shape coefficient is taken as 1.

[0024] The temperature field distribution of the terminal overlap part satisfies the following formula, namely: Q ( )= , ( ) (3) In formula (3), Indicates any point of the terminal overlap ( )’s thermodynamic temperature, Indicates the terminal overlap position along The thermal conductivity in the direction, Indicates the terminal overlap position along The thermal conductivity in the direction, Indicates the terminal overlap position along The thermal conductivity in the direction, Q ( ) indicates any point of the terminal overlap ( ) absorbs heat, Indicates the material density of the terminal overlap. Indicates the specific heat capacity of the material at the terminal joint. Indicates time, Indicates field.

[0025] In actual use, the embodiment of the present application establishes a three-dimensional finite element model of the DC charging terminal to be tested through the ANSYS parameterization program, and uses the solid227 unit in ANSYS to alternately calculate the temperature field and current field of the terminal overlap part. The unit contains 10 nodes, each node has 4 degrees of freedom, and can perform transient and steady-state thermoelectric coupling analysis on the terminal overlap part. Specifically, the embodiment of the present application divides the three-dimensional finite element model into tetrahedral units using free meshes, and uses ANSYS intelligent size control technology to control the size and density distribution of the mesh. Due to the mutual influence between the temperature field and the current field, a certain field cannot be solved alone, so a direct coupling method must be used for solution. Before solving, first specify the analysis type as transient analysis and turn on the time integration effect; secondly, define the load step and end time according to the time required for the DC charging terminal to be tested to reach stable operation, for example, the end time is 4 hours; then define the load current, the load change mode is step load, and turn on the automatic time step function. The initial temperature of the DC charging terminal to be tested is set to a uniform temperature field of room temperature 20°C, and the temperature at both ends of the DC charging terminal to be tested is approximately maintained at 20°C. When loading, all nodes on a certain end face of the DC charging terminal to be tested are selected to load the current vector, with the direction perpendicular to the end face. At the same time, the voltage of the node on the end face is defined to be constant to zero, and all nodes on the other end face are coupled with the voltage degree of freedom.

[0026] S102, comparing the maximum temperature rise value corresponding to the current design parameter with the preset temperature rise limit value, and when the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, calculating the reliability corresponding to the current design parameter.

[0027] For example, the preset temperature rise limit value ranges from 60K to 200K, and its value can be obtained through a temperature rise limit analysis test. Since the charging terminal materials corresponding to the UHV DC converter station are mostly pure copper or aluminum alloy, the embodiment of the present application takes the test piece made of aluminum alloy as an example. First, 200# and 400# fine sandpaper are used to polish and remove the oxide layer on the surface of the test piece, the polished surface is cleaned with acetone, and it is wiped clean with a clean white cotton cloth or toilet paper, and 0.2mm power composite grease is applied to the overlapping part of the test piece; secondly, the connection resistance of the overlapping part of the test piece is measured; then, the current is loaded to 1500A, and the current is passed for 4 hours to observe the temperature change of the overlapping part of the test piece. If the temperature is stable, the heating is continued for 10 hours to observe the change of the test piece. If the temperature rise of the test piece shows an obvious inflection point after heating for 10 hours, the temperature of the inflection point is the preset temperature rise limit value. If the temperature rise of the test piece does not show an obvious inflection point after heating for 10 hours, the current is returned to 0 and the current is continued to be loaded to 2000A and 2500A, and the above steps are repeated until the temperature rise of the overlapping part of the test piece shows an obvious inflection point.

[0028] For another example, the embodiment of the present application can use the pre-trained first neural network model to perform function fitting on the current design parameters and the maximum temperature rise value corresponding to the current design parameters, and calculate the reliability corresponding to the current design parameters through the Monte Carlo method. It should be noted that the training process of the neural network model is a conventional method and will not be described in detail. Figure 2 As shown, the first neural network model includes but is not limited to 5 input layer neurons, 12 hidden layer neurons and 1 output layer neuron, the activation function of the hidden layer neurons in the first neural network model is the Sigmoid function, the activation function of the output layer neurons in the first neural network model is the Purelin function, the initial weights are selected as random numbers between (-1, 1), the learning rate is selected in the range of 0.01 to 0.8, the momentum factor is selected in the range of 0.9 to 0.95, and the convergence condition is that the total network error E≤1.0×10 -12 , where the input parameter of the input layer neuron is the terminal length X of the current design parameter 1 、Terminal width X 2 、Terminal thickness X 3 , Terminal overlap resistance X 4 and terminal load current X 5 The output parameter of the output layer neuron is the maximum temperature rise value T corresponding to the current design parameter max For another example, the Monte Carlo method obtains the mean and standard deviation of the charging terminal under the limit state by performing random sampling and probability analysis on the fitting data, thereby obtaining the reliability corresponding to the current design parameters. The simulation times of the Monte Carlo method can be 5000 times, and normalization is performed when calculating the reliability.

[0029] S103, comparing the reliability corresponding to the current design parameter with the preset reliability limit value, and when the reliability corresponding to the current design parameter is greater than or equal to the preset reliability limit value, obtaining different design parameters in a finite neighborhood with the current design parameter as the origin for combined iteration until the maximum temperature rise value corresponding to the combined design parameter is greater than or equal to the preset temperature rise limit value.

[0030] For example, the preset reliability limit value is set according to the actual situation, and its value may be 92% or 99%, etc.; for another example, the range of the limited neighborhood may also be set according to the actual situation, such as increasing the terminal length by 0.5 cm, the terminal width by 0.5 cm, and the terminal thickness by 0.5 cm, etc., and the combined iteration includes processes such as simulating the temperature rise of the overlapped parts and comparing the maximum temperature rise value with the preset temperature rise limit value. For the principle, please refer to the above records and will not be repeated here.

[0031] Furthermore, if there are multiple combinations of design parameters whose corresponding maximum temperature rise values ​​are greater than or equal to the preset temperature rise limit value, the reliability and manufacturing cost corresponding to each combination of design parameters are obtained, wherein the reliability corresponding to the combination of design parameters is obtained by fitting the combination of design parameters and the maximum temperature rise values ​​corresponding to the combination of design parameters using a pre-trained second neural network model, and is calculated by the Monte Carlo method. It should be noted that the training process of the neural network model is a conventional method and will not be described in detail. Figure 3 As shown, the second neural network model includes but is not limited to 5 input layer neurons, 9 hidden layer neurons and 1 output layer neuron, the activation function of the hidden layer neurons in the second neural network model and the activation function of the output layer neurons in the second neural network model are both Sigmoid functions, the initial weights are selected as random numbers between (-1, 1), the learning rate is selected in the range of 0.01 to 0.9, the momentum factor is selected in the range of 0.85 to 0.98, and the convergence condition is that the total network error E≤1.0×10 -12 , where the input parameter of the input layer neuron is the terminal length X of the combined design parameter 1 、Terminal width X 2 、Terminal thickness X 3 , Terminal overlap resistance X 4 and terminal load current X 5 , the output parameter of the output layer neuron is the reliability R corresponding to the combined design parameter. Then, according to the reliability and manufacturing cost corresponding to the combined design parameter, the final design parameter is selected from multiple combined design parameters, for example, the reliability corresponding to the combined design parameters are sorted from high to low, and the combined design parameter with the lowest manufacturing cost is selected as the final design parameter, thereby ensuring excellent performance of the charging terminal and reducing the manufacturing cost.

[0032] The parameter optimization method provided in the embodiment of the present application safely and efficiently simulates the temperature rise process of the terminal overlap part of the DC charging terminal to be tested under the current design parameter conditions by means of a finite element model, obtains the maximum temperature rise value corresponding to the current design parameter, and then compares the maximum temperature rise value corresponding to the current design parameter with the preset temperature rise limit value. If the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, it indicates that the performance of the DC charging terminal to be tested does not meet the requirements, then further compares the reliability corresponding to the current design parameter with the preset reliability limit value, and when the reliability corresponding to the current design parameter is greater than or equal to the preset reliability limit value, it indicates that the design parameter can be optimized. At this time, different design parameters in a finite neighborhood with the current design parameter as the origin are combined and iterated until the maximum temperature rise value corresponding to the combined design parameter is greater than or equal to the preset temperature rise limit value, thereby improving the performance of the charging terminal, which can provide a basis for the subsequent revision of the automobile charging standard and ensure charging safety.

[0033] Based on the above embodiments, the present application provides a parameter optimization device for a DC charging terminal of an electric vehicle. The parameter optimization device 100 for a DC charging terminal of an electric vehicle can be applied to Figure 1~Figure 3 In the parameter optimization method of the corresponding embodiment. Please refer to Figure 4 , the parameter optimization device 100 for the DC charging terminal of an electric vehicle comprises a simulation module 101, a first comparison module 102 and a second comparison module 103 connected in sequence; The simulation module 101 is configured to obtain the current design parameters of the DC charging terminal to be tested, and simulate the temperature rise process of the terminal overlap part under the current design parameter conditions through a finite element model to obtain the maximum temperature rise value corresponding to the current design parameters, and the current design parameters include terminal length, terminal width, terminal thickness, terminal overlap part resistance and terminal loading current; The first comparison module 102 is configured to compare the maximum temperature rise value corresponding to the current design parameter with the preset temperature rise limit value, and when the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, calculate the reliability corresponding to the current design parameter; Furthermore, the second comparison module 103 is configured to compare the reliability corresponding to the current design parameter with the preset reliability limit value, and when the reliability corresponding to the current design parameter is greater than or equal to the preset reliability limit value, obtain different design parameters within a finite neighborhood with the current design parameter as the origin for combined iteration until the maximum temperature rise value corresponding to the combined design parameter is greater than or equal to the preset temperature rise limit value.

[0034] Optionally, in some embodiments of the present application, during the temperature rise process of the terminal overlap portion under the current design parameter conditions simulated by the finite element model, the current density of the terminal overlap portion satisfies the following formula: + =0; in, Indicates the conductivity of the terminal joint. Indicates any point of the terminal overlap ( )’s potential; The heat absorbed by the terminal overlap part satisfies the following formula: Q = ; in, Indicates the heat generated by the internal heat source of the terminal. = , Indicates the terminal overlap position along The electric field strength in the direction Indicates the terminal overlap position along The electric field strength in the direction Indicates the terminal overlap position along The electric field strength in the direction; Indicates the heat dissipated from the terminal to the surrounding medium. =acon* ( ), acon represents the convection heat dissipation coefficient, the unit is W / (m 2 ℃), Indicates the convection heat dissipation area, in m 2 , Indicates the temperature of the heating element in °C. Indicates the ambient temperature in °C; Indicates the amount of heat dissipated by the terminal through thermal radiation. , represents the emissivity, which is 0.92. represents the Stefan-Boltzmann constant, which is 5.67×10 -8 W / (m 2 ·K 4 ), Indicates the radiation surface The area of Indicates that the radiation surface To the radiating surface The shape coefficient is taken as 1; The temperature field distribution of the terminal overlap part satisfies the following formula: Q ( )= , ( ) ; in, Indicates any point of the terminal overlap ( )’s thermodynamic temperature, Indicates the terminal overlap position along The thermal conductivity in the direction, Indicates the terminal overlap position along The thermal conductivity in the direction, Indicates the terminal overlap position along The thermal conductivity in the direction, Q ( ) indicates any point of the terminal overlap ( ) absorbs heat, Indicates the material density of the terminal overlap. Indicates the specific heat capacity of the material at the terminal joint. Indicates time, Indicates field.

[0035] Optionally, in some embodiments of the present application, the first comparison module 102 is specifically used to use a pre-trained first neural network model to perform function fitting on the current design parameters and the maximum temperature rise values ​​corresponding to the current design parameters, and to obtain the reliability corresponding to the current design parameters through Monte Carlo calculation.

[0036] Optionally, in some embodiments of the present application, the first neural network model includes 5 input layer neurons, 12 hidden layer neurons and 1 output layer neuron, the activation function of the hidden layer neurons in the first neural network model is the Sigmoid function, and the activation function of the output layer neurons in the first neural network model is the Purelin function.

[0037] Alternatively, if Figure 5 As shown, in some embodiments of the present application, the parameter optimization device 100 for the DC charging terminal of the electric vehicle further includes an acquisition module 104 and a screening module 105 connected in sequence; The acquisition module 104 is configured to acquire the reliability and manufacturing cost corresponding to each of the combined design parameters if the maximum temperature rise values ​​corresponding to the multiple combined design parameters are greater than or equal to the preset temperature rise limit value, and the reliability corresponding to the combined design parameters is obtained by performing function fitting on the combined design parameters and the maximum temperature rise values ​​corresponding to the combined design parameters using a pre-trained second neural network model, and is calculated by the Monte Carlo method; And, the screening module 105 is configured to screen out final design parameters from a plurality of combined design parameters according to the reliability and manufacturing cost corresponding to the combined design parameters.

[0038] Optionally, in some embodiments of the present application, the second neural network model includes 5 input layer neurons, 9 hidden layer neurons and 1 output layer neuron, and the activation function of the hidden layer neurons in the second neural network model and the activation function of the output layer neurons in the second neural network model are both Sigmoid functions.

[0039] It should be noted that, for the description of the same steps and the same contents in this embodiment as those in other embodiments, reference can be made to the description in other embodiments and will not be repeated here.

[0040] The parameter optimization device provided in the embodiment of the present application safely and efficiently simulates the temperature rise process of the terminal overlap part of the DC charging terminal to be tested under the current design parameter conditions by means of a finite element model, obtains the maximum temperature rise value corresponding to the current design parameter, and then compares the maximum temperature rise value corresponding to the current design parameter with the preset temperature rise limit value. If the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, it indicates that the performance of the DC charging terminal to be tested does not meet the requirements, then further compares the reliability corresponding to the current design parameter with the preset reliability limit value, and when the reliability corresponding to the current design parameter is greater than or equal to the preset reliability limit value, it indicates that the design parameter can be optimized. At this time, different design parameters in a finite neighborhood with the current design parameter as the origin are combined and iterated until the maximum temperature rise value corresponding to the combined design parameter is greater than or equal to the preset temperature rise limit value, thereby improving the performance of the charging terminal, and can provide a basis for the subsequent revision of the automobile charging standard to ensure charging safety.

[0041] As another aspect, the present application embodiment provides a testing device. Figure 6 The test device 200 may include a processor 201 and a memory 202. The memory 202 stores at least one instruction, at least one program, code set or instruction set, which is loaded and executed by the processor 201 to implement Figure 1~Figure 3 The steps of the parameter optimization method of the corresponding embodiment.

[0042] As another aspect, the present application provides a computer-readable storage medium for storing program code, the program code for executing the aforementioned Figure 1~Figure 3 Any implementation of the parameter optimization method of the corresponding embodiment.

[0043] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0044] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms. The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0045] In addition, each functional module in each embodiment of the present application may be integrated into a processing unit, or each module may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.

[0046] Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the parameter optimization method of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0047] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A parameter optimization method for a DC charging terminal of an electric vehicle, characterized in that: The method comprises: Acquire current design parameters of the DC charging terminal to be tested, and simulate the temperature rise process of the terminal overlap part under the current design parameter conditions through a finite element model to obtain the maximum temperature rise value corresponding to the current design parameters, wherein the current design parameters include terminal length, terminal width, terminal thickness, terminal overlap part resistance, and terminal loading current; Comparing the maximum temperature rise value corresponding to the current design parameter with the preset temperature rise limit value, and when the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, calculating the reliability corresponding to the current design parameter; Compare the reliability corresponding to the current design parameter with the preset reliability limit value, and when the reliability corresponding to the current design parameter is greater than or equal to the preset reliability limit value, obtain different design parameters in a finite neighborhood with the current design parameter as the origin for combined iteration until the maximum temperature rise value corresponding to the combined design parameter is greater than or equal to the preset temperature rise limit value.

2. The method according to claim 1, characterized in that In the process of simulating the temperature rise of the terminal overlap portion under the current design parameter conditions by the finite element model, the current density of the terminal overlap portion satisfies the following formula: + =0； in, Indicates the conductivity of the terminal joint. Indicates any point of the terminal overlap ( )’s potential; The absorbed heat of the terminal overlap portion satisfies the following formula: Q = ； in, Indicates the heat generated by the internal heat source of the terminal. = , Indicates the terminal overlap position along The electric field strength in the direction Indicates the terminal overlap position along The electric field strength in the direction Indicates the terminal overlap position along The electric field strength in the direction; Indicates the heat dissipated from the terminal to the surrounding medium. =acon* ( ), acon represents the convection heat dissipation coefficient, the unit is W / (m 2 ℃), Indicates the convection heat dissipation area, in m 2 , Indicates the temperature of the heating element in °C. Indicates the ambient temperature in °C; Indicates the amount of heat dissipated by the terminal through thermal radiation. , represents the emissivity, which is 0.

92. represents the Stefan-Boltzmann constant, which is 5.67×10 -8 W / (m 2 ·K 4 ), Indicates the radiation surface The area of Indicates that the radiation surface To the radiating surface The shape coefficient is taken as 1; The temperature field distribution of the terminal overlap portion satisfies the following formula: Q ( )= ，( ) ； in, Indicates any point of the terminal overlap ( )’s thermodynamic temperature, Indicates the terminal overlap position along The thermal conductivity in the direction, Indicates the terminal overlap position along The thermal conductivity in the direction, Indicates the terminal overlap position along The thermal conductivity in the direction, Q ( ) indicates any point of the terminal overlap ( ) absorbs heat, Indicates the material density of the terminal overlap. Indicates the specific heat capacity of the material at the terminal overlap. Indicates time, Indicates field.

3. The method according to claim 1, characterized in that When the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, calculating the reliability corresponding to the current design parameter includes: A pre-trained first neural network model is used to perform function fitting on the current design parameters and the maximum temperature rise values ​​corresponding to the current design parameters, and the reliability corresponding to the current design parameters is obtained by Monte Carlo calculation.

4. The method according to claim 3, characterized in that The first neural network model includes 5 input layer neurons, 12 hidden layer neurons and 1 output layer neuron. The activation function of the hidden layer neurons in the first neural network model is the Sigmoid function, and the activation function of the output layer neurons in the first neural network model is the Purelin function.

5. The method according to any one of claims 1 to 4, characterized in that: The method further includes obtaining the reliability and manufacturing cost corresponding to each of the combined design parameters if the maximum temperature rise values ​​corresponding to multiple combined design parameters are greater than or equal to the preset temperature rise limit value, wherein the reliability corresponding to the combined design parameters is obtained by performing function fitting on the combined design parameters and the maximum temperature rise values ​​corresponding to the combined design parameters using a pre-trained second neural network model and calculating by a Monte Carlo method; According to the reliability corresponding to the combined design parameters and the manufacturing cost, a final design parameter is selected from the plurality of combined design parameters.

6. The method according to claim 5, characterized in that The second neural network model includes 5 input layer neurons, 9 hidden layer neurons and 1 output layer neuron, and the activation function of the hidden layer neurons in the second neural network model and the activation function of the output layer neurons in the second neural network model are both Sigmoid functions.

7. A parameter optimization device for a DC charging terminal of an electric vehicle, characterized in that: The device comprises a simulation module, a first comparison module and a second comparison module connected in sequence; The simulation module is configured to obtain current design parameters of the DC charging terminal to be tested, and simulate the temperature rise process of the terminal overlap part under the current design parameter conditions through a finite element model to obtain the maximum temperature rise value corresponding to the current design parameters, wherein the current design parameters include terminal length, terminal width, terminal thickness, terminal overlap part resistance, and terminal loading current; The first comparison module is configured to compare the maximum temperature rise value corresponding to the current design parameter with the preset temperature rise limit value, and when the maximum temperature rise value corresponding to the current design parameter is less than the preset temperature rise limit value, calculate the reliability corresponding to the current design parameter; Furthermore, the second comparison module is configured to compare the reliability corresponding to the current design parameter with a preset reliability limit value, and when the reliability corresponding to the current design parameter is greater than or equal to the preset reliability limit value, obtain different design parameters within a finite neighborhood with the current design parameter as the origin for combined iteration until the maximum temperature rise value corresponding to the combined design parameter is greater than or equal to the preset temperature rise limit value.

8. The device according to claim 7, characterized in that The first comparison module is specifically used to use a pre-trained first neural network model to perform function fitting on the current design parameters and the maximum temperature rise values ​​corresponding to the current design parameters, and to obtain the reliability corresponding to the current design parameters through Monte Carlo calculation.

9. The device according to any one of claims 7 to 8, characterized in that The device also includes an acquisition module and a screening module connected in sequence; The acquisition module is configured to acquire the reliability and manufacturing cost corresponding to each of the combined design parameters if the maximum temperature rise values ​​corresponding to multiple combined design parameters are greater than or equal to the preset temperature rise limit value, and the reliability corresponding to the combined design parameters is obtained by performing function fitting on the combined design parameters and the maximum temperature rise values ​​corresponding to the combined design parameters using a pre-trained second neural network model, and is calculated by a Monte Carlo method; Furthermore, the screening module is configured to screen out final design parameters from the multiple combined design parameters according to the reliability corresponding to the combined design parameters and the manufacturing cost.

10. A testing device, characterized in that: The testing device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the steps of any one of the methods described in claims 1 to 6.

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