General fluid medium development method, apparatus, and storage medium

Abstract

A general fluid medium development method of the application is applied to the field of new energy vehicle thermal management simulation, and solves the problems of limited medium selection and low cross-software calling efficiency in traditional simulation. The application takes Refprop as the basis for property calculation, generates specified amount of independent variable data and matches dependent variable data, and after screening, the fitting equation with R_square >= 0.995 is obtained by segment fitting according to liquid, gas and two-phase state. Through 10% random test data verification, when the average error is <= 1% and the maximum error is <= 2%, the fitting equation is packaged as a function and integrated into a medium model library. The application constructs a custom medium library through the fitting equation, does not need cross-software iterative calling, improves the simulation efficiency, can extend more than 100 kinds of pure substances and mixed media, the medium selection is flexible, and the fitting equation has high accuracy under boundary conditions, and meets the modeling and development needs of various thermal management simulation software.

Description

Technical Field

[0001] This invention relates to the field of fluid media development technology, and specifically to a general fluid media development method, equipment, and storage medium. Background Technology

[0002] Thermal management in new energy vehicles is a crucial technology for ensuring efficient operation, extending lifespan, and enhancing user experience in electric and hybrid vehicles. Its primary task is to regulate and optimize the temperature of the battery, drive motor, power electronics, and passenger compartment to ensure system safety and efficiency. The battery thermal management system controls battery temperature through liquid or gas cooling technology to prevent overheating or overcooling, thereby improving charge / discharge performance and extending battery life. Thermal management of the drive motor and power electronics utilizes efficient coolants to remove generated heat, ensuring stable system operation. Passenger compartment thermal management employs heat pump systems, PTC heaters, and waste heat recovery technology to provide a comfortable temperature environment inside the vehicle while minimizing the impact on driving range.

[0003] The thermal management system of new energy vehicles is crucial for ensuring the temperature stability of core components such as batteries, motors, and electronic controls under different operating conditions. Thermal management media, as key substances for heat transfer and regulation, play an indispensable role. The media used in the thermal management of new energy vehicles mainly include liquids, gases, refrigerants, solids, and some new materials. Each medium has different advantages and applicable scenarios based on its physical properties and application requirements.

[0004] Liquid media are the most common thermal management media, particularly suitable for efficient heat transfer. Liquid cooling systems are typically used to cool batteries, motors, and electronic control systems. Common liquid media include coolants and insulating coolants. Coolants are mixtures of water and antifreeze additives (such as ethylene glycol or propylene glycol), possessing excellent thermal conductivity, a low freezing point, and corrosion resistance, effectively preventing the cooling system from freezing in low-temperature winter environments. Insulating coolants are used in applications where they come into direct contact with high-voltage battery systems. These coolants have low electrical conductivity, effectively isolating electrical components and preventing electrical short circuits and electric shock risks, making them suitable for high-voltage batteries and electronic control systems. With technological advancements, phase change materials (PCMs) are increasingly being applied to battery thermal management systems. PCMs absorb or release heat through solid-liquid phase changes at specific temperatures, effectively mitigating thermal fluctuations during battery charging and discharging, improving battery efficiency, and extending its lifespan. Gas media play an important role in low-power heat dissipation and auxiliary cooling, particularly suitable for temperature control systems of motors and certain electronic components. Air is the most common gaseous medium. Heat is carried away by natural convection or forced airflow, making it suitable for applications with low heat dissipation requirements, such as small motors and low-power electronic devices. Hydrogen and helium, as gases with high thermal conductivity, are more expensive, but in certain special applications, such as high-efficiency motor cooling, they have superior thermal conductivity and can effectively improve heat dissipation efficiency. Refrigerants are the core medium in the air conditioning and heat pump systems of new energy vehicles, playing a role in heat transfer. Common refrigerants such as R134a and R1234yf are widely used in automotive air conditioning systems. They have good heat exchange performance, can efficiently regulate the temperature inside the vehicle, and comply with environmental regulations, reducing negative environmental impacts. In recent years, CO2 refrigerant (R744) has gradually gained attention due to its high efficiency and environmental friendliness. As a natural refrigerant, CO2 refrigerant has zero ozone depletion and low global warming potential, making it more suitable for high-pressure heat pump systems. Although it operates at higher pressures, its excellent heat exchange performance makes it an important choice for future air conditioning systems in new energy vehicles.

[0005] Thermal management simulation plays a crucial role in the design and optimization process of new energy vehicles. It simulates the heat transfer processes of various thermal management systems within the vehicle using virtual models, helping engineers predict and analyze temperature changes and thermal behavior of key components such as batteries, motors, and electronic control systems. Through simulation, the efficiency of the thermal management system under different operating conditions can be accurately evaluated, potential overheating problems identified, and the layout and design of the cooling system optimized, thereby improving the overall vehicle safety, performance, and energy efficiency. Thermal management simulation not only reduces the cost and time of physical testing but also supports comparative analysis of multiple design schemes. The physical properties of the medium involved play a decisive role in the accuracy of the simulation calculations.

[0006] Traditional method: In thermal management simulations for new energy vehicles, the medium is typically implemented by directly calling commercially available media libraries built into the software or by using external media libraries such as Refprop via interfaces. These include coolant, refrigerant, gaseous, and solid media. Calling a medium in the simulation software usually involves defining its thermal properties, such as thermal conductivity, specific heat capacity, density, viscosity, and other heat transfer characteristics, flow behavior, and phase change processes. Appropriate physical models are selected based on the type of medium (e.g., liquid, gas, refrigerant, or phase change material). Users need to configure the initial conditions (such as temperature, pressure, and flow rate) of these media and set their contact conditions with components such as heat sources and radiators in the simulation interface. Through these configurations, the simulation software couples the thermal behavior of the medium with the operating state of the entire thermal management system, simulating the heat transfer process of the medium under different operating environments, thereby helping to optimize the thermal management design. Common simulation tools, such as CFD (Computational Fluid Dynamics) software and thermal management simulation platforms, typically provide a media database, allowing users to directly select suitable media or customize their properties to suit specific application scenarios.

[0007] Disadvantages of traditional methods: 1. In traditional new energy vehicle thermal management simulation, some drag-and-drop modeling simulation software, such as Amesim, packages the media library into the software for direct selection and use. However, such software has low degree of freedom, poor support for model development, and cannot develop new models or add new media. 2. Some simulation software based on code modeling, such as MATLAB and dymola, provide a few very basic media, which limits the range of choices for users. They can also call professional media databases such as refprop through software interfaces. However, the thermal management of the whole vehicle involves a complex phase change heat transfer process, which is very complicated. Multiple iterative calls between software are required for each calculation step, resulting in very low simulation efficiency. Summary of the Invention

[0008] The present invention proposes a general method, device and storage medium for developing fluid media, which can at least solve one of the technical problems in the background art.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: A general method for developing fluid media, S1: Selecting the target fluid medium and its corresponding property function using working fluid property calculation software, and determining the boundary range of each state of the target fluid medium; S2. Generate independent variable data through algorithm writing, where the number of single independent variable data is 500-1000 and the number of multi-independent variable data is 4000-6000. S3. Use the working fluid property calculation software to obtain dependent variable data that matches the independent variable data, and ensure that the units of the independent variable data and the dependent variable data are consistent. S4. Filter the independent variable data and dependent variable data according to the preset segmentation conditions; S5. Divide the filtered data into segments according to phase and fit them to obtain a fitting equation, and the R-square of the fitting equation is ≥0.995. S6. Repeat steps S2 and S3 to obtain test data accounting for 10% of the total data volume, and use the test data to test and verify the fitting equation. S7. If the test verification results show that the average error is ≤1% and the maximum error is ≤2%, then write the development document and save the fitting equation in function form, and integrate all the fitting equations required for thermal management simulation into a medium model library; if the test verification results do not meet the above error requirements, then return to step S4, re-divide the range of independent variables or phase states, and refit the data after filtering.

[0010] Furthermore, the working fluid property calculation software is Refprop software, and the target fluid medium includes pure material medium and mixed medium, and the medium type includes coolant, refrigerant, mixed gas and other fluid media used in the thermal management of new energy vehicles.

[0011] Furthermore, in S2, the independent variable data is generated using a Python plugin or Matlab software.

[0012] Furthermore, in S5, piecewise fitting of the data is achieved using Matlab software.

[0013] Furthermore, in S5, the phase state division specifically involves dividing the data into liquid, gas, and two-phase regions, and independently fitting the data for each phase state region, resulting in a corresponding fitting equation for each phase state region.

[0014] Furthermore, the integrated media model library in S7 is encapsulated in Modelica code form, which can be directly applied to the modeling and calculation of thermal management simulation software without calling external media library software.

[0015] Furthermore, the physical property function includes one or more of the following: thermodynamic state solution, basic physical property calculation, saturation state solution, and heat transfer characteristic parameter calculation. The heat transfer characteristic parameter includes at least one of thermal conductivity, specific heat capacity, density, viscosity, and surface tension.

[0016] Furthermore, in S6, the test data is obtained by randomly sampling the total data to ensure the randomness and representativeness of the test data.

[0017] Furthermore, the method is applicable to the development of media models for various new energy vehicle thermal management simulation software such as Amesim, Matlab, and Dymola, and can realize the addition and customization of new media models.

[0018] Furthermore, the media model library can integrate fitting equations for more than 100 kinds of pure material media and their mixed media, and the accuracy of the calculation data of each fitting equation under boundary conditions is consistent with the original data of the working fluid property calculation software.

[0019] As can be seen from the above technical solution, the general fluid medium development method of the present invention, after exporting the medium function data, divides and segments it according to its characteristics and fits it into a binary multivariate property equation to form a custom medium function library. During use, it avoids the problem of reduced efficiency caused by data interaction between software. At the same time, it can produce and integrate more than 100 kinds of pure material media and their mixed media, and the media selection is free and flexible.

[0020] Specifically, the beneficial effects of the present invention are as follows: 1. Completely eliminates cross-software real-time interactive calls, significantly improving the efficiency of thermal management simulation calculations. Traditional simulation methods rely on real-time interaction with Refprop property software via an interface. Each simulation iteration requires repeated data transfer between the simulation software and Refprop. In multi-component coupled vehicle simulations, this high frequency of interaction and significant data transmission redundancy directly slows down the simulation computation. This invention uses raw Refprop property data as the data source, pre-generating fixed property calculation formulas through piecewise fitting. The fitted equations are then encapsulated as Modelica standard functions and integrated into the local media model library. During simulation, there is no need for external calls to Refprop or cross-software communication iterations. The simulation software directly calls the built-in formulas to complete the real-time property solution, eliminating cross-software data interaction losses at the source. The simulation efficiency for complex models such as vehicle thermal management systems and battery heat pump coupling can be improved by more than 50%, significantly shortening the iteration and simulation cycle.

[0021] 2. Wide media coverage, enabling rapid customization and development of over a hundred pure substances and mixed media, with highly flexible media selection. Existing commercial simulation software (Amesim, Dymola, Matlab) has fixed and limited built-in media libraries: drag-and-drop simulation software only supports pre-installed media and cannot add self-developed coolants or new environmentally friendly refrigerants; code-based simulation software has a limited range of basic media categories, and mixed media are virtually nonexistent. This method, relying on Refprop's comprehensive property database, can batch-compile property modeling development for over 100 pure fluid media and arbitrarily proportioned mixed media, adapting to all categories of fluids in new energy thermal management: ethylene glycol / propylene glycol-based coolants, R134a / R1234yf / CO2 (R744) environmentally friendly refrigerants, hydrogen / nitrogen mixed cooling gases, and insulating new phase change cooling media. Researchers can customize new media as needed and quickly integrate them into simulations, breaking the category limitations of commercial software's built-in media libraries and adapting to the forward-looking R&D needs of new thermal management media.

[0022] 3. Independent segmented fitting of liquid / gas / two-phase three regions + rigorous dual-index verification, resulting in excellent accuracy in physical property calculations across all operating conditions (including boundary and extreme conditions). 1) Layered Data Processing: Unlike global single-formula fitting which suffers from accuracy failure in phase transition and near-critical regions, this invention collects data and performs independent polynomial fitting for three thermodynamic phase zones: liquid, gas, and two-phase. Each phase zone is equipped with a dedicated property equation to accurately adapt to abrupt property changes such as phase transitions and near-critical conditions. 2) Strict Control of Fitting Indicators: Data for single / multiple independent variables is collected according to specifications (500-1000 sets for single independent variables and 4000-6000 sets for multiple independent variables). The fitting criterion is the coefficient of determination (R² ≥ 0.995) to ensure the fit of the single equation. 3) Random Sample Validation: 10% of the data is randomly selected from the total dataset as blind test samples. Only those that meet the criteria of **average calculation error of the entire medium ≤ 1% and maximum error of a single point ≤ 2%** are included in the database. If the criteria are not met, the data is backtracked and re-screened, and the segmentation intervals are adjusted. Under multiple quality control conditions, the final fitted equations in the database show a high degree of agreement with the original Refprop physical property data under boundary extreme conditions such as medium temperature and pressure limits. This solves the problem of distortion of critical and saturated boundary parameters in traditional simplified physical property formulas, and ensures the accuracy of thermal management simulation under high temperature, low temperature and high pressure extreme conditions.

[0023] 4. Standardized Modelica packaging, compatible with all mainstream thermal management simulation software and various modeling scenarios. The verified physical property functions are uniformly encapsulated into a standard Modelica code format media library. Modelica, as an open-source general-purpose simulation programming language, can be seamlessly integrated with mainstream new energy thermal management simulation software across the industry, including Amesim, Dymola, and Matlab / Simulink. It is also compatible with both drag-and-drop visual modeling and code-based custom modeling development modes. Enterprises no longer need to repeatedly develop multiple versions of media models for different simulation software. A single media library can be reused across all platforms, reducing the workload of secondary development for multi-software adaptation. It can be used for simulation of whole vehicle heat pump systems, battery liquid cooling systems, and also for refined simulation of motor oil cooling and electronic control cooling, covering a wide range of application scenarios.

[0024] 5. Reduce the cost of implementing simulation projects and decrease investment in physical calibration tests. Traditional novel refrigerant development requires extensive prototype calibration tests to obtain physical property parameters, resulting in high costs for testing materials, prototype fabrication, and temperature control testing. This invention leverages the mature and authoritative Refprop database to generate data, replacing most preliminary property assessment tests with mathematical fitting. Only a small number of prototypes are needed for verification, significantly reducing the cost of physical testing during the development of new refrigerants and coolants. Furthermore, the refrigerant library can be repeatedly iterated and reused, eliminating the need for repeated full-process development of similar refrigerants in subsequent projects, thus shortening the new product development cycle and reducing project development costs.

[0025] 6. Achieve independent control over the media model, freeing it from the copyright and version restrictions of commercial software media libraries. Commercial simulation software's built-in media library is subject to the software vendor's copyright restrictions. Software version upgrades may incur additional costs such as changes in media parameters and paid expansion of the media library. In contrast, all media models in this solution are built based on self-developed fitting equations, and all physical property codes are stored locally. The addition or removal of media and the modification of parameters are completely autonomous and controllable, and are not restricted by software vendor versions or pricing policies. This makes it easier for car manufacturers and component suppliers to build their own proprietary thermal management media databases. Attached Figure Description

[0026] Figure 1 This is a flowchart of the present invention; Figure 2a Refprop software media selection diagram; Figure 2b Refprop software media property diagram; Figure 3 This is a schematic diagram of the MATLAB independent variable generation interface; Figure 4a Schematic diagram of media property unit settings in Refprop software; Figure 4b Refprop software media property type setting diagram; Figure 4cA schematic diagram of the Refprop software interface for acquiring media properties; Detailed Implementation To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0027] like Figure 1 The diagram shows the complete logical flow of the fluid medium in this invention, from data source acquisition, data processing, segmented fitting, accuracy verification to medium library encapsulation. First, the medium boundary parameters are determined using Refprop. Quantitative independent variables are generated using Python / Matlab algorithms. Then, Refprop is called to match dependent variable data of the same unit. After data filtering, segmented fitting is performed according to phase state. Once the goodness of fit satisfies (Rsquare ≥ 0.995), 10% of the data is randomly selected for error verification. If the average error is ≤ 1% and the maximum error is ≤ 2%, the fitting formula is encapsulated as a Function and a Modelica format medium library is generated, along with development documentation. If the error does not meet the standards, the process returns to the data filtering stage to re-divide the intervals and iteratively optimize.

[0028] Specifically, the general fluid medium development method described in this embodiment includes the following steps: (1) Using working fluid property calculation software (Refprop is used as an example here), select the required medium and property function, check the applicable range of the medium, and determine the boundary range of each state of the medium; such as Figure 2a , 2b As shown; (2) Obtain independent variable data (through Python plugins or algorithm development). 500-1000 single independent variables, 4000-6000 multiple independent variables; (3) Use Refprop to obtain the dependent variable data (note that the units should be consistent). (4) Filter data according to segmentation conditions; (5) The data is divided into segments according to phase state and fitted to obtain the equation R_square>0.995. This process is implemented by MATLAB, and the code is attached. (6) Repeat steps 2-3 to obtain 10% of the test data and perform test verification; (7) If the average error is less than 1% and the maximum error is less than 2%, the function fitting meets the requirements and the development document is written; if it does not meet the requirements, repeat step 4, divide the data according to the range of independent variables or phase, and refit the function.

[0029] (8) Save the fitted function as a function and integrate all the functions required for simulation calculation into a media model library.

[0030] This invention develops a media library by fitting equations to data, thereby improving simulation efficiency. By extracting property function values ​​from professional media software such as Refprop, the freedom of media selection can be greatly expanded. By traversing data points within the applicable range and using a segmented and partitioned fitting method, the accuracy of the media model can be improved, achieving extremely high data accuracy even under boundary conditions.

[0031] The method described in this embodiment of the invention is applicable to the development of media models for various new energy vehicle thermal management simulation software such as Amesim, Matlab, and Dymola, and can realize the addition and customization of new media models.

[0032] Table 1 is a table of refrigerant properties required for thermal management simulation; Table 1

[0033] The following is the MATLAB code for fitting property functions: aaa=30;% significant figures line=4; %z is the column number MMM = readtable('99.xlsx'); %%%%%%%%%%%%%%%%%All variable tables generated successfully% ... % ... [m,n]=size(MMM); MMMNumvector = randperm(m); MMMNumEnd=round(0.1*m); MM = MMM(MMMNumvector(1:MMMNumEnd), 1:n); % Reserved test set M=MMM(MMMNumvector(1+MMMNumEnd:end),1:n);% Fitting set %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [Mm, Mn] = size(M); s_2phase = {}; k_2phase = 0; s_gas = {}; k_gas = 0; s_liquid = {}; k_liquid = 0; for i = 1:size(M, 1) if string(table2array(M(i, 3))) == '2-Phase' k_2phase = k_2phase + 1; for j = 1:size(M, 2) s_2phase{k_2phase, j} = table2array(M(i, j)); end end; if string(table2array(M(i, 3))) == 'Gas' % Supercritical k_gas = k_gas + 1; for j = 1:size(M, 2) s_gas{k_gas, j} = table2array(M(i, j)); end end; if string(table2array(M(i, 3))) == 'Liquid' k_liquid = k_liquid + 1; for j = 1:size(M, 2) s_liquid{k_liquid, j} = table2array(M(i, j)); end end; end % Array element packet splitting % Liquid region [Mm, Mn] = size(s_liquid); Numvector = randperm(Mm); NumEnd = round(0.9 * Mm); Mfit = s_liquid(Numvector(1:NumEnd), 1:Mn); % Experimental set Mtest=s_liquid(Numvector(1+NumEnd:end),1:Mn);%Test set [Mfitm, Mfitn] = size(Mfit); [Mtestm, Mtestn] = size(Mtest); %Gaseous region [Vm,Vn]=size(s_gas); Vumvector = randperm(Vm); VumEnd=round(0.8*Vm); Vfit=s_gas(Vumvector(1:VumEnd),1:Vn);%Experiment set Vtest=s_gas(Vumvector(1+VumEnd:end),1:Vn);%Test set [Vfitm, Vfitn] = size(Vfit); [Vtestm, Vtestn] = size(Vtest); % Two-phase region [Tm,Tn]=size(s_2phase); Tumvector = randperm(Tm); TumEnd=round(0.8*Tm); Tfit=s_2phase(Tumvector(1:TumEnd),1:Tn);%Experiment set Ttest=s_2phase(Tumvector(1+TumEnd:end),1:Tn);%Test set [Tfitm, Tfitn] = size(Tfit); [Ttestm, Ttestn] = size(Ttest); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% xM=cell2mat(Mfit(:,1)); yM=cell2mat(Mfit(:,2)); ifiscell(Mfit(2,line))==1 zM=str2double(string(Mfit(:,line))); else zM = cell2mat(Mfit(:, line)); end [fM, rM] = fit([xM, yM], zM, 'poly55'); syms x; %% Expression output starts syms y; FFM = vpa(fM.p00 + fM.p10 * x + fM.p01 * y + fM.p20 * x^2 + fM.p11 * x * y + fM.p02 * y^2 + fM.p30 * x^3 + fM.p21 * x^2 * y + fM.p12 * x * y^2 + fM.p03 * y^3 + fM.p40 * x^4 + fM.p31 * x^3 * y + fM.p22 * x^2 * y^2 + fM.p13 * x * y^3 + fM.p04 * y^4 + fM.p50 * x^5 + fM.p41 * x^4 * y + fM.p32 * x^3 * y^2 + fM.p23 * x^2 * y^3 + fM.p14 * x * y^4 + fM.p05 * y^5, aaa); FFM_temp{1} = FFM; FFM5 = vpa(fM.p00 + fM.p10 * x + fM.p01 * y + fM.p20 * x^2 + fM.p11 * x * y + fM.p02 * y^2 + fM.p30 * x^3 + fM.p21 * x^2 * y + fM.p12 * x * y^2 + fM.p03 * y^3 + fM.p40 * x^4 + fM.p31 * x^3 * y + fM.p22 * x^2 * y^2 + fM.p13 * x * y^3 + fM.p04 * y^4 + fM.p50 * x^5 + fM.p41 * x^4 * y + fM.p32 * x^3 * y^2 + fM.p23 * x^2 * y^3 + fM.p14 * x * y^4 + fM.p05 * y^5, 5); FFM5_temp{1} = FFM5; fM_temp{1} = fM; %%%%%% xV = cell2mat(Vfit(:, 1)); yV = cell2mat(Vfit(:, 2)); if iscell(Vfit(2, line)) == 1 zV = str2double(string(Vfit(:, line))); else zV = cell2mat(Vfit(:,line)); end [fV,rV] = fit([xV,yV],zV,'poly55'); syms x; %% Expression output starts syms y; FFV = vpa(fV.p00 + fV.p10 * x + fV.p01 * y + fV.p20 * x^2 + fV.p11 * x * y + fV.p02 * y^2 + fV.p30 * x^3 + fV.p21 * x^2 * y + fV.p12 * x * y^2 + fV.p03 * y^3 + fV.p40 * x^4 + fV.p31 * x^3 * y + fV.p22 * x^2 * y^2 + fV.p13 * x * y^3 + fV.p04 * y^4 + fV.p50 * x^5 + fV.p41 * x^4 * y + fV.p32 * x^3 * y^2 + fV.p23 * x^2 * y^3 + fV.p14 * x * y^4 + fV.p05 * y^5, aaa); FFM_temp{3} = FFV; FFV5 = vpa(fV.p00 + fV.p10 * x + fV.p01 * y + fV.p20 * x^2 + fV.p11 * x * y + fV.p02 * y^2 + fV.p30 * x^3 + fV.p21 * x^2 * y + fV.p12 * x * y^2 + fV.p03 * y^3 + fV.p40 * x^4 + fV.p31 * x^3 * y + fV.p22 * x^2 * y^2 + fV.p13 * x * y^3 + fV.p04 * y^4 + fV.p50 * x^5 + fV.p41 * x^4 * y + fV.p32 * x^3 * y^2 + fV.p23 * x^2 * y^3 + fV.p14 * x * y^4 + fV.p05 * y^5, 5); FFM5_temp{3} = FFV5; fV_temp{1} = fV; %%%%%%%% xT = cell2mat(Tfit(:,1)); yT = cell2mat(Tfit(:,2)); if iscell(Tfit(2,line)) == 1 zT = str2double(string(Tfit(:,line))); else zT = cell2mat(Tfit(:, line)); end [fT, rT] = fit([xT, yT], zT, 'poly55'); syms x; %% Expression output starts syms y; FFT = vpa(fT.p00 + fT.p10 * x + fT.p01 * y + fT.p20 * x^2 + fT.p11 * x * y + fT.p02 * y^2 + fT.p30 * x^3 + fT.p21 * x^2 * y + fT.p12 * x * y^2 + fT.p03 * y^3 + fT.p40 * x^4 + fT.p31 * x^3 * y + fT.p22 * x^2 * y^2 + fT.p13 * x * y^3 + fT.p04 * y^4 + fT.p50 * x^5 + fT.p41 * x^4 * y + fT.p32 * x^3 * y^2 + fT.p23 * x^2 * y^3 + fT.p14 * x * y^4 + fT.p05 * y^5, aaa); FFM_temp{2} = FFT; FFT5 = vpa(fT.p00 + fT.p10 * x + fT.p01 * y + fT.p20 * x^2 + fT.p11 * x * y + fT.p02 * y^2 + fT.p30 * x^3 + fT.p21 * x^2 * y + fT.p12 * x * y^2 + fT.p03 * y^3 + fT.p40 * x^4 + fT.p31 * x^3 * y + fT.p22 * x^2 * y^2 + fT.p13 * x * y^3 + fT.p04 * y^4 + fT.p50 * x^5 + fT.p41 * x^4 * y + fT.p32 * x^3 * y^2 + fT.p23 * x^2 * y^3 + fT.p14 * x * y^4 + fT.p05 * y^5, 5); FFM5_temp{2} = FFT5; fT_temp{1} = fT; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % TEST data processing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% xtestM = cell2mat(Mtest(:, 1)); ytestM = cell2mat(Mtest(:, 2)); if iscell(Mtest(2, line)) == 1 ztestM = str2double(string(Mtest(:, line))); else ztestM = cell2mat(Mtest(:, line)); end %%%%%% xtestV = cell2mat(Vtest(:, 1)); ytestV = cell2mat(Vtest(:, 2)); if iscell(Vtest(2, line)) == 1 ztestV = str2double(string(Vtest(:, line))); else ztestV = cell2mat(Vtest(:, line)); end %%%%%%% xtestT = cell2mat(Ttest(:, 1)); ytestT = cell2mat(Ttest(:, 2)); if iscell(Ttest(2, line)) == 1 ztestT = str2double(string(Ttest(:, line))); else ztestT = cell2mat(Ttest(:, line)); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % TEST data verification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for j = 1:size(ztestM, 1) FM(j) = fM(xtestM(j), ytestM(j)); aveM(j) = (FM(j) - ztestM(j)) / ztestM(j); end aM=sum(abs(aveM)) / size(ztestM,1)*100; amaxM = max(aveM) * 100; forj=1:size(ztestV,1) FV(j)=fV(xtestV(j),ytestV(j)); aveV(j)=(FV(j)-ztestV(j)) / ztestV(j); end aV=sum(abs(aveV)) / size(ztestV,1)*100; amaxV = max(aveV) * 100; forj=1:size(ztestT,1) FT(j)=fT(xtestT(j),ytestT(j)); aveT(j)=(FT(j)-ztestT(j)) / ztestT(j); end aT=sum(abs(aveT)) / size(ztestT,1)*100; amaxT = max(aveT) * 100.

[0034] Figure 2a and Figure 2b Refprop software media selection and property curve diagram; such as Figure 2a The image shows the fluid medium selection interface of the Refprop software. The interface has a built-in index of all fluid categories and can be filtered by commonly used refrigerants, natural gas, cryogenic working fluids, calibration fluids, etc. It supports the selection of over a hundred commonly used fluids for new energy thermal management, such as water, alkanes, and R32 / R1234yf / CO2. It serves as the data source entry point for the target medium selection in this solution. Figure 2b The figure shows the pressure-enthalpy property curve of R134a refrigerant output by Refprop. The figure contains multiple sets of isothermal property curves from 210K to 490K, which intuitively show the property changes of the refrigerant in liquid, gas, and two-phase phases in different temperature and pressure ranges. It is used in step S1 to determine the boundary range of the target medium temperature, pressure, and other operating conditions.

[0035] Figure 3This diagram illustrates the Matlab interface for the programmed generation of independent variables. It shows the editor interface for batch generation of independent variable parameters using Matlab code in step S2 of this invention. The interface allows for the customization of upper and lower limits for pressure and enthalpy, as well as sampling intervals. A loop algorithm is used to automatically output a batch dataset of pressure-enthalpy binary independent variables, strictly meeting the data volume requirements of 500-1000 single independent variables and 4000-6000 multiple independent variables. A Python plugin can also be used to achieve the same independent variable generation function, providing standardized sampling nodes for subsequent physical property data acquisition in this scheme.

[0036] Figure 4a , Figure 4b , Figure 4c A schematic diagram illustrating the configuration of Refprop physical property parameters and the export of raw data; such as Figure 4a The image shows the Refprop unit settings interface, which allows for unified configuration of the units of measurement for all physical properties, including temperature, pressure, viscosity, thermal conductivity, and surface tension. This ensures consistency in the unit formats of the independent and dependent variables in step S3, avoiding data fitting errors caused by inconsistent units. Figure 4b The image shows the Refprop property selection configuration interface, which allows users to select all the property indicators listed in Table 1 required for thermal management, such as density, specific heat capacity, dynamic viscosity, saturation parameters, and isothermal compressibility, precisely matching the calculation requirements of the 43 property functions for new energy thermal management simulation; for example... Figure 4c The image shows the interface of the raw physical property data table exported by Refprop. The table stores the original measured physical property data of the medium under different temperature and pressure conditions. After export, it is saved to Excel and used as the original data source for Matlab piecewise fitting.

[0037] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0038] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0039] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the general fluid media development methods described above.

[0040] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0041] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0042] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0043] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0044] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A general method for developing fluid media, characterized in that, Includes the following steps, S1. Select the target fluid medium and its corresponding property function using working fluid property calculation software, and determine the boundary range of each state of the target fluid medium; S2. Generate independent variable data through algorithm writing, where the number of single independent variable data is 500-1000 and the number of multi-independent variable data is 4000-6000. S3. Use the working fluid property calculation software to obtain dependent variable data that matches the independent variable data, and ensure that the units of the independent variable data and the dependent variable data are consistent. S4. Filter the independent variable data and dependent variable data according to the preset segmentation conditions; S5. Divide the filtered data into segments according to phase and fit them to obtain a fitting equation, and the R-square of the fitting equation is ≥0.

995. S6. Repeat steps S2 and S3 to obtain test data accounting for 10% of the total data volume, and use the test data to test and verify the fitting equation. S7. If the test verification results show that the average error is ≤1% and the maximum error is ≤2%, then write the development document and save the fitting equation in function form, and integrate all the fitting equations required for thermal management simulation into a medium model library; if the test verification results do not meet the above error requirements, then return to step S4, re-divide the range of independent variables or phase states, and refit the data after filtering.

2. The general fluid medium development method according to claim 1, characterized in that: The working fluid property calculation software is Refprop software, and the target fluid medium includes pure material medium and mixed medium, and the medium type includes coolant, refrigerant, and mixed gas, which are fluid media used for thermal management of new energy vehicles.

3. The general fluid medium development method according to claim 1, characterized in that: In S2, independent variable data can be generated using Python plugins or Matlab software.

4. The general fluid medium development method according to claim 1, characterized in that: In S5, piecewise fitting of data is achieved using Matlab software.

5. The general fluid medium development method according to claim 1, characterized in that: In S5, the phase division specifically involves dividing the data into liquid, gas, and two-phase regions, and independently fitting the data for each phase region, resulting in a corresponding fitting equation for each phase region.

6. The general fluid medium development method according to claim 1, characterized in that: The integrated media model library in S7 is packaged in Modelica code form and can be directly applied to the modeling and calculation of thermal management simulation software without calling external media library software.

7. The general fluid medium development method according to claim 1, characterized in that: The physical property function includes one or more of the following: thermodynamic state solution, basic physical property calculation, saturation state solution, and heat transfer characteristic parameter calculation. The heat transfer characteristic parameter includes at least one of thermal conductivity, specific heat capacity, density, viscosity, and surface tension.

8. The general fluid medium development method according to claim 1, characterized in that: In S6, test data is obtained by randomly sampling from the total data to ensure the randomness and representativeness of the test data.

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