One-dimensional simulation model automatic calibration method and system applied to thermal management system

Abstract

The invention relates to the technical field of thermal management, and discloses a one-dimensional simulation model automatic calibration method and system applied to a thermal management system, and the method comprises the steps: obtaining a plurality of calibration parameters, and taking each calibration parameter as a current calibration parameter one by one for iteration; determining a plurality of current calibration values of the current calibration parameter according to the first current upper and lower limits; simulating the one-dimensional simulation model based on each current calibration value to generate a corresponding simulation result; based on each simulation result, determining a corresponding model deviation; a fitting quadratic curve is generated based on the current calibration values and the model deviations corresponding to the current calibration values, the first current upper and lower limits are iterated based on the fitting quadratic curve, and iteration of the current calibration parameters is completed; the target model deviation smaller than the specified deviation in the model deviations is recognized, calibration of the one-dimensional simulation model is achieved based on the first target calibration value combination, and the technical problems that a related calibration method is poor in flexibility and insufficient in generalization ability are solved.

Description

Technical Field

[0001] The present application relates to the field of thermal management technology, and in particular to a method and system for automatically calibrating a one-dimensional simulation model applied to a thermal management system. Background Art

[0002] Simulation technology is an indispensable tool in modern engineering and scientific research, especially in the design, analysis, and optimization of complex systems. One-dimensional simulation, as an important branch of simulation technology, is widely used in many fields such as fluid mechanics, heat conduction, and structural dynamics due to its advantages such as high computational efficiency, simple model, and ease of implementation. One-dimensional simulation improves computational efficiency by simplifying the model. However, this simplification may not fully capture the complex phenomena in the actual system, so there is a deviation between the simulation results and the measured results. By adjusting the calibration parameters in the model, the simulation results are made as consistent as possible with the measured results. Parameter calibration is the core step to ensure the credibility of simulation results. If the calibration accuracy is low, the model may deviate from the actual system behavior. In practical applications, parameter calibration often faces great difficulties due to factors such as measurement errors and model simplification. Summary of the Invention

[0003] The present application provides a one-dimensional simulation model automatic calibration method and system for thermal management systems, which solves the technical problems of poor flexibility and insufficient generalization ability of related calibration methods, and achieves the technical effect of improving the generalization ability of the model.

[0004] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, an embodiment of the present application provides a method for automatic calibration of a one-dimensional simulation model applied to a thermal management system, the method comprising: obtaining a plurality of calibration parameters of the one-dimensional simulation model, iterating each of the calibration parameters one by one as a current calibration parameter, and obtaining the initial upper and lower limits of the current calibration parameters; based on the initial upper and lower limits, generating a first current upper and lower limit that is dynamically adjusted; according to the first current upper and lower limits, determining a plurality of current calibration values of the current calibration parameter; based on each of the current calibration values, performing a thermal management simulation on the one-dimensional simulation model to generate a corresponding simulation result; and determining the model deviation based on the deviation between the simulation result and the corresponding measured result. Difference, the model deviation is used to characterize the calibration accuracy of the one-dimensional simulation model; determine whether each of the model deviations is greater than the specified deviation, if so, generate a fitting quadratic curve based on each of the current calibration values and the model deviations corresponding to each of the current calibration values, iterate the first current upper and lower limits based on the fitting quadratic curve, and complete the iteration of the current calibration parameters; if not, identify the target model deviation that is smaller than the specified deviation in each of the model deviations, determine the calibration values of each of the calibration parameters corresponding to the target model deviation as a first target calibration value combination, and based on the first target calibration value combination, realize the calibration of the one-dimensional simulation model.

[0006] The one-dimensional simulation model automatic calibration method provided in this embodiment includes: obtaining multiple calibration parameters of the one-dimensional simulation model, and iterating each of the calibration parameters one by one as the current calibration parameter; obtaining the initial upper and lower limits of the current calibration parameter, and generating a first current upper and lower limit of dynamic adjustment based on the initial upper and lower limits; determining multiple current calibration values of the current calibration parameter according to the first current upper and lower limits; performing thermal management simulation on the one-dimensional simulation model based on each of the current calibration values to generate corresponding simulation results; determining a model deviation based on the deviation between the simulation result and the corresponding measured result, and the model deviation is used to characterize the calibration accuracy of the one-dimensional simulation model; judging each of the Whether the model deviations are all greater than the specified deviations, if so, a fitting quadratic curve is generated based on each of the current calibration values and the model deviations corresponding to each of the current calibration values, the first current upper and lower limits are iterated based on the fitting quadratic curve, and the iteration of the current calibration parameters is completed; if not, the target model deviations that are smaller than the specified deviations in each of the model deviations are identified, the calibration values of each of the calibration parameters corresponding to the target model deviations are determined as the first target calibration value combination, and based on the first target calibration value combination, the calibration of the one-dimensional simulation model is realized, which solves the technical problems of poor flexibility and insufficient generalization ability of the relevant calibration methods, and achieves the technical effect of improving the generalization ability of the model.

[0007] Optionally, based on the first current upper and lower limits, multiple calibration values of the current calibration parameters are determined, including: determining the sampling number of the current calibration value based on the range width of the first current upper and lower limits; based on the sampling number, uniformly sampling within the range of the first current upper and lower limits to generate multiple current calibration values.

[0008] Optionally, the method also includes: if the sampling number of the current calibration value is 1, completing the calibration of the current calibration parameter, and determining the current calibration value as the target calibration value; if the calibration of each calibration parameter has been completed, determining the target calibration value of each calibration parameter as a second target calibration value combination, and based on the second target calibration value combination, realizing the calibration of the one-dimensional simulation model.

[0009] Optionally, the method further includes: dividing the multiple calibration parameters into the current calibration parameters, the parameters to be calibrated and the calibration parameters that have been calibrated; obtaining the second current upper and lower limits of the parameters to be calibrated; when iterating the current calibration parameters based on the multiple current calibration values, the calibration value of the parameter to be calibrated is set to the midpoint of the second current upper and lower limits, and the calibration value of the calibration parameter that has been calibrated is set to the target calibration value.

[0010] Optionally, the number of samples of the current calibration value is determined based on the range width of the first current upper and lower limits, including: if the range width is less than or equal to 0.001, the number of samples is set to 1; if the range width is greater than 0.001 and less than or equal to 0.1, the number of samples is set to 3; if the range width is greater than 0.1 and less than or equal to 0.5, the number of samples is set to 5; if the range width is greater than 0.5 and less than or equal to 1, the number of samples is set to 7; if the range width is greater than 1 and less than or equal to 4, the number of samples is set to 9.

[0011] Optionally, a fitting quadratic curve is generated based on each of the current calibration values and the model deviation corresponding to each of the current calibration values, and the first current upper and lower limits are iterated based on the fitting quadratic curve, including: using the least squares method, with the current calibration value as the independent variable and the model deviation as the dependent variable, to generate the fitting quadratic curve, and obtain the quadratic term coefficient and the horizontal coordinate value of the vertex of the fitting quadratic curve; based on the horizontal coordinate value of the vertex, extracting the shorter distance between the current upper limit and the current lower limit of the vertex to the first current upper and lower limits, and using the extracted shorter distance as the first parameter value; extracting the smaller value between 0.25 times the range width and the first parameter value, and using the extracted smaller value as the second parameter value; identifying the current minimum model deviation in each of the model deviations; if the quadratic term coefficient is less than the specified coefficient, then according to the corresponding value of the current minimum model deviation The horizontal coordinate value and the second parameter value iterate the first current upper and lower limits; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is within the range of the first current upper and lower limits, then according to the horizontal coordinate value of the vertex and the second parameter value, the first current upper and lower limits are iterated; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is less than the second parameter value, then according to the horizontal coordinate value of the vertex and the second parameter value, the first current upper and lower limits are iterated; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is greater than or equal to the second parameter value, then according to the current upper limit or the current lower limit, and the range width, the first current upper and lower limits are iterated.

[0012] Optionally, the method further includes: if the quadratic term coefficient is less than a specified coefficient, iterating the range of the first current upper and lower limits to [low-gap, low+gap] based on the abscissa value corresponding to the current minimum model deviation and the second parameter value; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the abscissa value of the vertex is within the range of the first current upper and lower limits, iterating the range of the first current upper and lower limits to [top-0.34×gap, top+0.34×gap] based on the abscissa value of the vertex and the second parameter value; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the abscissa value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is less than the second parameter value, iterating the range of the first current upper and lower limits to [top-0.34×gap, top+0.34×gap] based on the abscissa value of the vertex and the second parameter value. 34×gap]; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is greater than or equal to the second parameter value, then according to the current upper limit or the current lower limit, and the range width, iterate the first current upper and lower limits, including: when the horizontal coordinate value of the vertex is less than the current lower limit, iterate the range of the first current upper and lower limits to [min-range, min]; when the horizontal coordinate value of the vertex is greater than the current upper limit, iterate the range of the first current upper and lower limits to [max, max+range]; wherein, the specified coefficient is set to 0.1; low is the horizontal coordinate value corresponding to the current minimum model deviation; gap is the second parameter value; top is the horizontal coordinate value of the vertex; min is the current lower limit; max is the current upper limit; range is the range width.

[0013] Optionally, the method also includes: if the total number of simulations reaches a set threshold, the calibration values of each calibration parameter corresponding to the global minimum model deviation are determined as a third target calibration value combination, and based on the third target calibration value combination, the calibration of the one-dimensional simulation model is achieved; wherein, the global minimum model deviation is the minimum value of each current minimum model deviation.

[0014] Optionally, the model deviation is determined in the following manner: when the one-dimensional simulation model is a steady-state model, the squares of the differences between each steady-state response value in the simulation result and the corresponding steady-state measured value are summed to obtain a steady-state model deviation; when the one-dimensional simulation model is a transient model, the squares of the differences between the transient response values at each time point in the simulation result and the corresponding transient target value are summed and then divided by the number of time points to obtain a transient model deviation.

[0015] In the second aspect, an embodiment of the present application provides an automatic calibration system for a one-dimensional simulation model, which can implement the above-mentioned one-dimensional simulation model automatic calibration method. The system includes: an acquisition module for acquiring multiple calibration parameters of the one-dimensional simulation model, and iterating each of the calibration parameters one by one as the current calibration parameter; and an initial upper and lower limits for obtaining the current calibration parameters; a generation module for generating a first current upper and lower limit for dynamic adjustment based on the initial upper and lower limits; and a multiple calibration values of the current calibration parameter based on the first current upper and lower limits; a simulation module for performing thermal management simulation on the one-dimensional simulation model based on each of the current calibration values to generate corresponding simulation results. ; and is used to determine the model deviation based on the deviation between the simulation result and the corresponding measured result; a judgment module is used to judge whether each of the model deviations is greater than the specified deviation. If so, a fitting quadratic curve is generated based on each of the current calibration values and the model deviations corresponding to each of the current calibration values, the first current upper and lower limits are iterated based on the fitting quadratic curve, and the iteration of the current calibration parameters is completed; if not, the target model deviation of each of the model deviations that is smaller than the specified deviation is identified, and the calibration values of each of the calibration parameters corresponding to the target model deviation are determined as a first target calibration value combination, and based on the first target calibration value combination, the calibration of the one-dimensional simulation model is realized.

[0016] In a third aspect, an embodiment of the present application provides a computer device comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the above-mentioned one-dimensional simulation model automatic calibration method by executing the computer instructions.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above-mentioned one-dimensional simulation model automatic calibration method.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions, which are used to enable a computer to execute the above-mentioned one-dimensional simulation model automatic calibration method. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 Flowchart of the one-dimensional simulation model automatic calibration method provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of a water-side pipeline flow calibration system provided in an embodiment of the present application;

[0022] Figure 3 Schematic diagram of the iteration of calibration parameters provided in the embodiment of the present application;

[0023] Figure 4 Schematic diagram of an automatic calibration system for a one-dimensional simulation model provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] To make the purpose, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are 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 those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0026] Simulation technology is an indispensable tool in modern engineering and scientific research, especially in the design, analysis, and optimization of complex systems. One-dimensional simulation, as an important branch of simulation technology, is widely used in many fields such as fluid mechanics, heat conduction, and structural dynamics due to its advantages such as high computational efficiency, simple model, and ease of implementation. One-dimensional simulation improves computational efficiency by simplifying the model. However, this simplification may not fully capture the complex phenomena in the actual system, so there is a deviation between the simulation results and the measured results. By adjusting the calibration parameters in the model, the simulation results are made as consistent as possible with the measured results. Parameter calibration is the core step to ensure the credibility of simulation results. If the calibration accuracy is low, the model may deviate from the actual system behavior. In practical applications, parameter calibration often faces great difficulties due to factors such as measurement errors and model simplification.

[0027] For example, a Chinese patent (CN111125909B) discloses an automated calibration method for a one-dimensional automotive thermal management model. Its calibration algorithm relies on third-party software provided by the MATLAB Optimization Toolbox. This technical solution merely integrates existing tools and does not propose an independent (i.e., independent of third-party tools) calibration algorithm. This results in insufficient model generalization and flexibility, making it difficult to meet the refined requirements of complex multi-parameter calibration.

[0028] An embodiment of the present application provides a method for automatic calibration of a one-dimensional simulation model applied to a thermal management system. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] In this embodiment, a one-dimensional simulation model automatic calibration method for a thermal management system is provided. Figure 1 , Figure 1 This is a flowchart of the one-dimensional simulation model automatic calibration method provided in the embodiment of the present application, such as Figure 1 As shown, the process includes the following steps:

[0030] Step S1: obtaining a plurality of calibration parameters of a one-dimensional simulation model, taking each calibration parameter as a current calibration parameter for iteration, and obtaining initial upper and lower limits of the current calibration parameter.

[0031] Each calibration parameter that requires calibration is iterated one by one. In each iteration, only the current calibration parameter is iterated. After completing one iteration of the current calibration parameter, the other calibration parameters are replaced with the current calibration parameter for iteration. After each calibration parameter has completed one iteration, one iteration round is considered complete. After completing one iteration round, the next round of calibration parameter iterations is performed. Iterating the current calibration parameter refers to iterating the first current upper and lower limits of the current calibration parameter. The initial upper and lower limits are input values and remain unchanged during the iteration process.

[0032] For example, a set of multiple calibration parameters is called a parameter set, which can be represented as {a, b, c, d}. First, calibration parameter a is iterated, and the parameter set is updated to {a_best, b, c, d}, where a_best represents the calibration value of calibration parameter a after one iteration. Then, calibration parameter b is iterated, resulting in {a_best, b_best, c, d}. And so on, after completing a round of iterations, {a_best, b_best, c_best, d_best} is obtained. In each iteration, only one calibration parameter is iterated, while the remaining calibration parameters remain fixed during the iterative calculation. With each iteration, the upper and lower limits of each calibration parameter gradually converge, resulting in an increasingly precise calibration range.

[0033] Step S3: generating dynamically adjusted first current upper and lower limits based on the initial upper and lower limits; and determining a plurality of current calibration values of the current calibration parameter according to the first current upper and lower limits.

[0034] The first current upper and lower limits are dynamically adjusted and continuously iterated. The range of the first current upper and lower limits gradually decreases during the iteration process. The values of the first current upper and lower limits are within the range of the initial upper and lower limits. Uniform sampling is performed within the range of the first current upper and lower limits to obtain a current calibration value, which is within the range of the first current upper and lower limits.

[0035] Step S5: Based on each of the current calibration values, perform thermal management simulation on the one-dimensional simulation model to generate corresponding simulation results; based on the deviation between the simulation results and the corresponding measured results, determine the model deviation, and the model deviation is used to characterize the calibration accuracy of the one-dimensional simulation model.

[0036] Each current calibration value is input into a one-dimensional simulation model for simulation, generating corresponding simulation results. The differences between the simulation results and the measured results are compared to determine the corresponding model deviation. This model deviation guides iterations to improve calibration accuracy. The above steps yield the model deviation corresponding to each current calibration value.

[0037] Step S7, determine whether each of the model deviations is greater than the specified deviation. If so, generate a fitting quadratic curve based on each of the current calibration values and the model deviations corresponding to each of the current calibration values, iterate the first current upper and lower limits based on the fitting quadratic curve, and complete the iteration of the current calibration parameters; if not, identify the target model deviations in each of the model deviations that are less than the specified deviation, determine the calibration values of each of the calibration parameters corresponding to the target model deviation as the first target calibration value combination, and based on the first target calibration value combination, realize the calibration of the one-dimensional simulation model.

[0038] If each of the model deviations is greater than the specified deviation, it means that the current minimum deviation is greater than the specified deviation. In the current iteration, the minimum deviation among the model deviations is recorded as the current minimum deviation. With each current calibration value as the horizontal coordinate value, and the model deviation corresponding to each current calibration value as the vertical coordinate, the least squares method is used for fitting to obtain a fitted quadratic curve. Based on the parameters in the fitted quadratic curve, such as the coordinates of the vertex, the coefficient of the quadratic term, etc., the first current upper and lower limits are iterated. After completing the iteration of the current calibration parameter, the next calibration parameter is iterated as the current calibration parameter, and so on, and each calibration parameter is iterated one by one as the current calibration parameter. The calibration values of each calibration parameter participate in the simulation calculation process.

[0039] If a target model deviation is smaller than the specified deviation among the model deviations, it means that the current minimum deviation is smaller than the specified deviation. The model deviation smaller than the specified deviation is referred to as the target model deviation. The calibration values of the calibration parameters corresponding to the target model deviation are determined as a first target calibration value combination. Based on the first target calibration value combination, calibration of the one-dimensional simulation model is achieved.

[0040] The one-dimensional simulation model automatic calibration method provided in this embodiment includes: obtaining multiple calibration parameters of the one-dimensional simulation model, and iterating each of the calibration parameters one by one as the current calibration parameter; obtaining the initial upper and lower limits of the current calibration parameter, and generating a first current upper and lower limit of dynamic adjustment based on the initial upper and lower limits; determining multiple current calibration values of the current calibration parameter according to the first current upper and lower limits; performing thermal management simulation on the one-dimensional simulation model based on each of the current calibration values to generate corresponding simulation results; determining a model deviation based on the deviation between the simulation result and the corresponding measured result, and the model deviation is used to characterize the calibration accuracy of the one-dimensional simulation model; judging each of the Whether the model deviations are all greater than the specified deviations, if so, a fitting quadratic curve is generated based on each of the current calibration values and the model deviations corresponding to each of the current calibration values, the first current upper and lower limits are iterated based on the fitting quadratic curve, and the iteration of the current calibration parameters is completed; if not, the target model deviations that are smaller than the specified deviations in each of the model deviations are identified, the calibration values of each of the calibration parameters corresponding to the target model deviations are determined as the first target calibration value combination, and based on the first target calibration value combination, the calibration of the one-dimensional simulation model is realized, which solves the technical problems of poor flexibility and insufficient generalization ability of the relevant calibration methods, and achieves the technical effect of improving the generalization ability of the model.

[0041] In some embodiments, multiple calibration values of the current calibration parameter are determined based on the first current upper and lower limits, including: determining the sampling number of the current calibration value based on the range width of the first current upper and lower limits; based on the sampling number, uniformly sampling within the range of the first current upper and lower limits to generate multiple current calibration values.

[0042] The first current upper and lower limits include the current upper limit and the current lower limit. The range width refers to the difference between the current upper limit and the current lower limit. The larger the range width, the more samples are taken; the smaller the range width, the fewer samples are taken. The number of samples is the number of current calibration values. The range of the first current upper and lower limits refers to the continuous interval consisting of the current upper limit and the current lower limit. Within the range of the first current upper and lower limits, a uniform sampling method is used to generate each current calibration value. The formula between the current calibration value and the current upper limit and the current lower limit is as follows:

[0043]

[0044] Among them, x i is the current calibration value; a is the current upper limit; b is the current lower limit; n is the number of samples.

[0045] In some embodiments, the method further includes: if the sampling number of the current calibration value is 1, the calibration of the current calibration parameter is completed, and the current calibration value is determined as the target calibration value; if the calibration of each calibration parameter has been completed, the target calibration value of each calibration parameter is determined as a second target calibration value combination, and based on the second target calibration value combination, the calibration of the one-dimensional simulation model is realized.

[0046] When the calibration of the current calibration parameter is completed, the iteration of the previously calibrated parameters will not be repeated during the subsequent iteration of other calibration parameters. The values of the previously calibrated calibration parameters remain unchanged and are fixed at the target calibration values. If some calibration parameters are calibrated first, it means that these calibration parameters have met the accuracy requirements and the iteration is exited first. When calibrating multiple calibration parameters, the calibration of some calibration parameters is completed first, and then the remaining calibration parameters are calibrated to avoid meaningless calculation work and thus prevent the calibration from taking too long.

[0047] If the number of samples of a calibration parameter is 1, it means that the calibration parameter has met the accuracy requirements and no iteration is required. If the number of samples of each calibration parameter is 1, it means that each calibration parameter has met the accuracy requirements and the iteration process will terminate.

[0048] In some embodiments, the method further includes: dividing the multiple calibration parameters into the current calibration parameters, the parameters to be calibrated, and the calibration parameters that have been calibrated; obtaining the second current upper and lower limits of the parameters to be calibrated; when iterating the current calibration parameters based on the multiple current calibration values, the calibration value of the parameter to be calibrated is set to the midpoint of the second current upper and lower limits, and the calibration value of the calibration parameter that has been calibrated is set to the target calibration value.

[0049] Among them, in the initial stage of iteration, each calibration parameter may not have completed the calibration, so there are only current calibration parameters and parameters to be calibrated. Parameters to be calibrated refer to calibration parameters that have not yet completed calibration and do not participate in the current iteration. During the iteration process, any calibration parameter must belong to one of the three categories of current calibration parameters, parameters to be calibrated, and calibration parameters that have completed calibration. During the iteration process of the current calibration parameters, the one-dimensional simulation model is simulated and calculated based on each current calibration value. Each calibration parameter participates in the simulation calculation process, wherein the calibration values of the parameters to be calibrated and the calibration parameters that have completed calibration remain unchanged during the process of each simulation calculation. The calibration value of the parameter to be calibrated is set to the midpoint of the second current upper and lower limits of the parameter to be calibrated, and the calibration value of the calibration parameter that has completed calibration is set to the target calibration value, all of which are fixed values.

[0050] In some embodiments, the number of samples of the current calibration value is determined based on the range width of the first current upper and lower limits, including: if the range width is less than or equal to 0.001, the number of samples is set to 1; if the range width is greater than 0.001 and less than or equal to 0.1, the number of samples is set to 3; if the range width is greater than 0.1 and less than or equal to 0.5, the number of samples is set to 5; if the range width is greater than 0.5 and less than or equal to 1, the number of samples is set to 7; if the range width is greater than 1 and less than or equal to 4, the number of samples is set to 9.

[0051] The number of samples is adjusted based on the range width and calculation accuracy. During the iteration process, the range between the first current upper and lower limits continues to narrow, and the number of samples continues to decrease. When the number of samples reaches 1, it indicates that the range between the first current upper and lower limits is small enough, and the current calibration parameters are no longer iterated, indicating that the calibration of the current calibration parameters has been completed.

[0052] In some embodiments, a fitting quadratic curve is generated based on each of the current calibration values and the model deviation corresponding to each of the current calibration values, and the first current upper and lower limits are iterated based on the fitting quadratic curve, including: using the least squares method, with the current calibration value as the independent variable and the model deviation as the dependent variable, to generate the fitting quadratic curve, and obtain the quadratic term coefficient and the horizontal coordinate value of the vertex of the fitting quadratic curve; based on the horizontal coordinate value of the vertex, extracting the shorter distance between the current upper limit and the current lower limit of the first current upper and lower limits of the vertex, and using the extracted shorter distance as the first parameter value; extracting the smaller value between 0.25 times the range width and the first parameter value, and using the extracted smaller value as the second parameter value; identifying the current minimum model deviation in each of the model deviations; if the quadratic term coefficient is less than the specified coefficient, then The first current upper and lower limits are iterated according to the corresponding horizontal coordinate value and the second parameter value; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is within the range of the first current upper and lower limits, then the first current upper and lower limits are iterated according to the horizontal coordinate value of the vertex and the second parameter value; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is less than the second parameter value, then the first current upper and lower limits are iterated according to the horizontal coordinate value of the vertex and the second parameter value; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is greater than or equal to the second parameter value, then the first current upper and lower limits are iterated according to the current upper limit or the current lower limit, and the range width.

[0053] Iterating the current calibration parameters refers to iterating the first current upper and lower limits of the current calibration parameters to obtain increasingly accurate upper and lower limit ranges. During the iteration of the current calibration parameters, multiple corresponding model deviations are obtained through simulation calculation based on multiple current calibration values. The minimum value among the model deviations obtained in the current iteration is called the current minimum model deviation.

[0054] Using the least squares method, the current calibration value is used as the independent variable and the model deviation as the dependent variable to generate a fitted quadratic curve. The equation for the fitted quadratic curve is constructed and the coefficients in the equation are solved, for example using matrix calculations, to obtain the horizontal coordinate of the lowest vertex and the quadratic coefficient. The vertical coordinate of the vertex is generally less than the current minimum model deviation.

[0055] In some embodiments, the method further includes: if the quadratic term coefficient is less than a specified coefficient, iterating the range of the first current upper and lower limits to [low-gap, low+gap] according to the horizontal coordinate value corresponding to the current minimum model deviation and the second parameter value; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is within the range of the first current upper and lower limits, iterating the range of the first current upper and lower limits to [top-0.34×gap, top+0.34×gap] according to the horizontal coordinate value of the vertex and the second parameter value; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is less than the second parameter value, iterating the range of the first current upper and lower limits to [top-0.34×gap, top+ 0.34×gap]; if the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is greater than or equal to the second parameter value, then according to the current upper limit or the current lower limit, and the range width, iterate the first current upper and lower limits, including: when the horizontal coordinate value of the vertex is less than the current lower limit, iterate the range of the first current upper and lower limits to [min-range, min]; when the horizontal coordinate value of the vertex is greater than the current upper limit, iterate the range of the first current upper and lower limits to [max, max+range]; wherein, the specified coefficient is set to 0.1; low is the horizontal coordinate value corresponding to the current minimum model deviation; gap is the second parameter value; top is the horizontal coordinate value of the vertex; min is the current lower limit; max is the current upper limit; range is the range width.

[0056] If the vertex is within the range of the first current upper and lower limits, the horizontal coordinate value of the vertex is between the current upper and lower limits. If the vertex is outside the range of the first current upper and lower limits, the horizontal coordinate value of the vertex is greater than the current upper limit or less than the current lower limit. When the coefficient of the quadratic term is greater than or equal to the specified coefficient, the vertex is used as the reference point to expand outward to obtain the updated range of the first current upper and lower limits. In addition, the second parameter value is continuously reduced during the iterative process. A lower limit value is set for the second parameter value. If the calculated second parameter value is less than the lower limit value, the lower limit value is used as the second parameter value.

[0057] In some embodiments, the method also includes: if the total number of simulations reaches a set threshold, the calibration values of each calibration parameter corresponding to the global minimum model deviation are determined as a third target calibration value combination, and based on the third target calibration value combination, the calibration of the one-dimensional simulation model is achieved; wherein, the global minimum model deviation is the minimum value of each current minimum model deviation.

[0058] The threshold can be set to 10,000 times, meaning a maximum of 10,000 simulations. Regardless of whether the current simulation result meets the accuracy requirements, the iterative process will be forced to terminate, avoiding infinite loops caused by simulation failures and saving computational costs. Each simulation can calculate the current minimum model deviation. Over multiple simulation iterations, the global minimum model deviation is the minimum of the current minimum model deviations. The calibration values of the calibration parameters corresponding to the minimum model deviation are extracted as the third target calibration value combination. Calibration of the one-dimensional simulation model is achieved based on this third target calibration value combination.

[0059] In some embodiments, the global minimum model deviation is determined in the following manner: using a global variable to store the global minimum model deviation; if the current minimum model deviation is less than the global minimum model deviation, updating the global minimum model deviation to the current minimum model deviation.

[0060] A global variable is used to store the global minimum model deviation. After each simulation, the current minimum model deviation is compared with the global minimum model deviation. If the current minimum model deviation is less than the global minimum model deviation, the global minimum model deviation is updated. Across multiple simulations, the global minimum model deviation is the minimum of the current minimum model deviations.

[0061] In some embodiments, the model deviation is determined in the following manner: when the one-dimensional simulation model is a steady-state model, the squares of the differences between each steady-state response value in the simulation result and the corresponding steady-state measured value are summed to obtain a steady-state model deviation; when the one-dimensional simulation model is a transient model, the squares of the differences between the transient response values at each time point in the simulation result and the corresponding transient target value are summed and then divided by the number of time points to obtain a transient model deviation.

[0062] Among them, if the one-dimensional simulation model is a steady-state model, the simulation result is a set of steady-state response values, and the measured result is a set of steady-state measured values. The deviation value of these two sets of values is calculated, and the difference between the elements at each position is squared and summed to obtain the steady-state model deviation.

[0063] If the one-dimensional simulation model is a transient model, the simulation result is a response curve, including the transient response value at each time point. The measured result is a measured curve, including the steady-state measured value corresponding to each time point. The deviation between the response curve and the measured curve at each time point is solved, and the square of the deviation at each time point is accumulated to obtain the sum of squares. The sum of squares is divided by the number of time points to obtain the average deviation value as the transient model deviation.

[0064] In some embodiments, a python script is used to call the API of the simulation software to achieve automatic calibration of the above-mentioned one-dimensional simulation model.

[0065] The following will be described in conjunction with specific embodiments:

[0066] Example 1

[0067] Example 1 is an application case of water side pipeline flow calibration, which belongs to the calibration of steady-state model. Figure 2 , Figure 2 This is a schematic diagram of the water side pipeline flow calibration system provided in the embodiment of the present application. Figure 2 As shown, Figure 2 The middle blue area represents the piping loop. ①, ②, ③, and ④ represent four holes, with corresponding diameters designated as Diameter 1, Diameter 2, Diameter 3, and Diameter 4, respectively. The diameters of the four holes affect the flow distribution within each loop. These diameters are used as calibration parameters, and the target flow rates for the three loops are used as the steady-state measured values. Python code is used to automatically calibrate the steady-state model, using the sum of the squares of the differences between the simulated flow rates (steady-state response values) and the target flow rates (steady-state measured values) for the three loops as the steady-state model deviation.

[0068] Please refer to Figure 3 , Figure 3 This is an iterative diagram of the calibration parameters provided in the embodiment of the present application. During the calibration process, the changes of each calibration parameter are as follows: Figure 3 As shown, diameter 1, diameter 2, diameter 3, and diameter 4 are iterated in sequence. After completing a round of iteration of calibration parameters, the upper and lower limits of each calibration parameter are gradually reduced, and each calibration parameter tends to be stable. After 124 iterations, all calibration parameters have been calibrated and the iteration is exited. The flow distribution accuracy can reach 99.89%. In the early stage of iteration, the quadratic curve fitted by the least squares method between each model deviation and the calibration value is similar to a parabola, which is in line with the actual engineering situation. During the iteration process, diameter 3 reaches a fixed value before other calibration parameters (that is, the current number of samples is 1), and diameter 3 exits the iteration first. In the subsequent iteration process of other calibration parameters, the calibration value of diameter 3 no longer changes. Through the above calibration method, when faced with multiple calibration parameters, the calibration of some calibration parameters is completed first, and then the remaining calibration parameters are calibrated, avoiding meaningless calculation work and causing the calibration to take too long.

[0069] Example 2

[0070] Example 2 is an application case for passenger compartment thermal load calibration, specifically transient model calibration. Because the boundaries of the passenger compartment thermal load model are complex and accurate data collection is impractical, model calibration is required to ensure that the cooling curve of the simulation model is consistent with the experimentally measured values. Because the relationships between the various calibration parameters of the passenger compartment model are unknown, all relevant parameters are listed as calibration parameters. Upper and lower limits for each calibration parameter are set based on engineering experience, and the average deviation at each time point is used as the transient model deviation.

[0071] In the crew compartment calibration, there are many calibration parameters and their relationships are unclear. There are errors between the simulation curve and the measured curve that cannot be fitted. Even if the number of simulation iterations reaches the set threshold of 10,000 times, the transient model deviation cannot be reduced. Iterating more times is meaningless and there are systematic errors.

[0072] Please refer to Figure 4 , Figure 4 Schematic diagram of the automatic calibration system of the one-dimensional simulation model provided in the embodiment of the present application. Figure 4 As shown, the embodiment of the present application provides an automatic calibration system for a one-dimensional simulation model, which can realize the above-mentioned automatic calibration method for a one-dimensional simulation model applied to a thermal management system, and the system includes: an acquisition module for acquiring multiple calibration parameters of the one-dimensional simulation model, and iterating each of the calibration parameters one by one as a current calibration parameter; and an initial upper and lower limit for acquiring the current calibration parameter; a generation module for generating a first current upper and lower limit for dynamic adjustment based on the initial upper and lower limits; and a plurality of calibration values for the current calibration parameter according to the first current upper and lower limits; a simulation module for performing a thermal management simulation on the one-dimensional simulation model based on each of the current calibration values to generate a corresponding simulation result; and a module for performing a thermal management simulation on the one-dimensional simulation model based on the current calibration values to generate a corresponding simulation result; and a module for performing a thermal management simulation on the one-dimensional simulation model based on the current calibration value to generate a corresponding simulation result. The deviation between the simulation result and the corresponding measured result is used to determine the model deviation, and the model deviation is used to characterize the calibration accuracy of the one-dimensional simulation model; a judgment module is used to judge whether each of the model deviations is greater than the specified deviation. If so, a fitting quadratic curve is generated based on each of the current calibration values and the model deviations corresponding to each of the current calibration values, the first current upper and lower limits are iterated based on the fitting quadratic curve, and the iteration of the current calibration parameters is completed; if not, the target model deviation of each of the model deviations that is smaller than the specified deviation is identified, and the calibration values of each of the calibration parameters corresponding to the target model deviation are determined as the first target calibration value combination, and based on the first target calibration value combination, the calibration of the one-dimensional simulation model is realized.

[0073] The further functional description of each of the above modules is the same as that of the above corresponding embodiments and will not be repeated here.

[0074] The automatic calibration system of the one-dimensional simulation model in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0075] See also Figure 5 , Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0076] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0077] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0078] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0079] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0080] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0081] The embodiments of the present application also provide a computer-readable storage medium. The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0082] An embodiment of the present application provides a computer program product, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a method according to any embodiment of the present application.

[0083] The systems or modules described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0084] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0085] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0086] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device that implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0089] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of other identical elements in the process, method, commodity, or apparatus comprising the element.

[0090] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences from the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0091] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

[0092] Although the embodiments of the present application have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A one-dimensional simulation model automatic calibration method for a thermal management system, characterized in that: The method comprises: Acquire multiple calibration parameters of the one-dimensional simulation model, iterate each calibration parameter one by one as a current calibration parameter, and obtain initial upper and lower limits of the current calibration parameter; Based on the initial upper and lower limits, generating dynamically adjusted first current upper and lower limits; and determining a plurality of current calibration values of the current calibration parameter according to the first current upper and lower limits; Based on each of the current calibration values, a thermal management simulation is performed on the one-dimensional simulation model to generate a corresponding simulation result; based on a deviation between the simulation result and the corresponding measured result, a model deviation is determined, wherein the model deviation is used to characterize the calibration accuracy of the one-dimensional simulation model; Determine whether each of the model deviations is greater than the specified deviation. If so, generate a fitting quadratic curve based on each of the current calibration values and the model deviations corresponding to each of the current calibration values, iterate the first current upper and lower limits based on the fitting quadratic curve, and complete the iteration of the current calibration parameters; if not, identify the target model deviation in each of the model deviations that is less than the specified deviation, determine the calibration value of each of the calibration parameters corresponding to the target model deviation as a first target calibration value combination, and based on the first target calibration value combination, realize the calibration of the one-dimensional simulation model.

2. The method according to claim 1, characterized in that Determining multiple calibration values of the current calibration parameter according to the first current upper and lower limits includes: Determining the number of samples of the current calibration value according to the range width of the first current upper and lower limits; Based on the number of samples, sampling is performed uniformly within the range of the first current upper and lower limits to generate a plurality of the current calibration values.

3. The method according to claim 2, characterized in that The method further comprises: If the sampling number of the current calibration value is 1, the calibration of the current calibration parameter is completed, and the current calibration value is determined as the target calibration value; if the calibration of each calibration parameter has been completed, the target calibration value of each calibration parameter is determined as a second target calibration value combination, and based on the second target calibration value combination, the calibration of the one-dimensional simulation model is realized.

4. The method according to claim 3, characterized in that The method further comprises: Dividing the plurality of calibration parameters into the current calibration parameters, parameters to be calibrated, and the calibration parameters that have been calibrated; Obtaining the second current upper and lower limits of the parameter to be calibrated; When the current calibration parameter is iterated based on multiple current calibration values, the calibration value of the parameter to be calibrated is set to the midpoint of the second current upper and lower limits, and the calibration value of the calibration parameter that has been calibrated is set to the target calibration value.

5. The method according to claim 2, characterized in that Determining the number of samples of the current calibration value according to the range width of the first current upper and lower limits includes: If the range width is less than or equal to 0.001, the number of samples is set to 1; if the range width is greater than 0.001 and less than or equal to 0.1, the number of samples is set to 3; if the range width is greater than 0.1 and less than or equal to 0.5, the number of samples is set to 5; if the range width is greater than 0.5 and less than or equal to 1, the number of samples is set to 7; if the range width is greater than 1 and less than or equal to 4, the number of samples is set to 9.

6. The method according to claim 2, characterized in that Generating a fitting quadratic curve based on each of the current calibration values and the model deviation corresponding to each of the current calibration values, and iterating the first current upper and lower limits based on the fitting quadratic curve, comprising: Using the least squares method, with the current calibration value as the independent variable and the model deviation as the dependent variable, the fitted quadratic curve is generated to obtain the quadratic term coefficient and the horizontal coordinate value of the vertex of the fitted quadratic curve; based on the horizontal coordinate value of the vertex, the shorter distance between the current upper limit and the current lower limit of the first current upper and lower limits is extracted from the vertex, and the extracted shorter distance is used as the first parameter value; the smaller value between 0.25 times the range width and the first parameter value is extracted, and the extracted smaller value is used as the second parameter value; and the current minimum model deviation among the respective model deviations is identified; If the quadratic term coefficient is less than the specified coefficient, iterating the first current upper and lower limits according to the abscissa value corresponding to the current minimum model deviation and the second parameter value; If the quadratic term coefficient is greater than or equal to the specified coefficient, and the abscissa value of the vertex is within the range of the first current upper and lower limits, iterating the first current upper and lower limits according to the abscissa value of the vertex and the second parameter value; If the quadratic term coefficient is greater than or equal to the specified coefficient, and the abscissa value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is less than the second parameter value, iterating the first current upper and lower limits based on the abscissa value of the vertex and the second parameter value; If the quadratic term coefficient is greater than or equal to the specified coefficient, and the horizontal coordinate value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is greater than or equal to the second parameter value, then the first current upper and lower limits are iterated based on the current upper limit or the current lower limit and the range width.

7. The method according to claim 6, characterized in that The method further comprises: If the quadratic term coefficient is less than the specified coefficient, iterating the first current upper and lower limits to [low-gap, low+gap] according to the abscissa value corresponding to the current minimum model deviation and the second parameter value; If the quadratic term coefficient is greater than or equal to the specified coefficient, and the abscissa value of the vertex is within the range of the first current upper and lower limits, then based on the abscissa value of the vertex and the second parameter value, the range of the first current upper and lower limits is iterated to [top-0.34×gap, top+0.34×gap]. If the quadratic term coefficient is greater than or equal to the specified coefficient, the abscissa value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is less than the second parameter value, then based on the abscissa value of the vertex and the second parameter value, the range of the first current upper and lower limits is iterated to [top-0.34×gap,top+0.34×gap]; If the coefficient of the quadratic term is greater than or equal to the specified coefficient, and the abscissa value of the vertex is outside the range of the first current upper and lower limits, and the first parameter value is greater than or equal to the second parameter value, then iterating the first current upper and lower limits according to the current upper and lower limits or the current lower limit and the range width, including: when the abscissa value of the vertex is less than the current lower limit, iterating the range of the first current upper and lower limits to be [min-range, min]; when the abscissa value of the vertex is greater than the current upper limit, iterating the range of the first current upper and lower limits to be [max, max+range]; Among them, the specified coefficient is set to 0.1; low is the horizontal coordinate value corresponding to the current minimum model deviation; gap is the second parameter value; top is the horizontal coordinate value of the vertex; min is the current lower limit; max is the current upper limit; range is the range width.

8. The method according to claim 6, characterized in that The method also includes: if the total number of simulations reaches a set threshold, determining the calibration values of each calibration parameter corresponding to the global minimum model deviation as a third target calibration value combination, and achieving calibration of the one-dimensional simulation model based on the third target calibration value combination; wherein the global minimum model deviation is the minimum value of each current minimum model deviation.

9. The method according to claim 1, characterized in that The model bias is determined as follows: In the case where the one-dimensional simulation model is a steady-state model, square and sum the differences between each steady-state response value in the simulation result and the corresponding steady-state measured value to obtain a steady-state model deviation; In the case where the one-dimensional simulation model is a transient model, the transient model deviation is obtained by square-sum-ing the differences between the transient response values at each time point in the simulation result and the corresponding transient target values and then dividing the sum by the number of time points.

10. An automatic calibration system for a one-dimensional simulation model, capable of implementing the automatic calibration method for a one-dimensional simulation model applied to a thermal management system according to any one of claims 1 to 9, characterized in that: The system comprises: An acquisition module, configured to acquire a plurality of calibration parameters of a one-dimensional simulation model, iterating each of the calibration parameters one by one as a current calibration parameter; and acquiring initial upper and lower limits of the current calibration parameters; a generating module, configured to generate dynamically adjusted first current upper and lower limits based on the initial upper and lower limits; and to determine a plurality of calibration values of the current calibration parameter according to the first current upper and lower limits; a simulation module, configured to perform a thermal management simulation on the one-dimensional simulation model based on each of the current calibration values to generate a corresponding simulation result; and to determine a model deviation based on a deviation between the simulation result and a corresponding measured result, wherein the model deviation is used to characterize the calibration accuracy of the one-dimensional simulation model; A judgment module is used to judge whether each of the model deviations is greater than a specified deviation. If so, a fitting quadratic curve is generated based on each of the current calibration values and the model deviations corresponding to each of the current calibration values, the first current upper and lower limits are iterated based on the fitting quadratic curve, and the iteration of the current calibration parameters is completed; if not, a target model deviation that is smaller than the specified deviation in each of the model deviations is identified, the calibration values of each of the calibration parameters corresponding to the target model deviation are determined as a first target calibration value combination, and based on the first target calibration value combination, the calibration of the one-dimensional simulation model is realized.

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