Energy storage container battery cabin air duct baffle parameter optimization design method

Abstract

The invention discloses an energy storage container battery compartment air duct baffle parameter optimization design method, which comprises the following steps of 1, constructing a battery compartment model, setting a baffle, and taking the length and the angle of the baffle as driving parameters; step 2, carrying out grid division and fluid domain extraction; 3, setting boundary conditions in the solver and solving parameters; 4, selecting an experimental design type, and performing multi-working-condition simulation; 5, determining a response variable, setting a response surface type, and fitting a response surface model equation; step 6, seeking the optimal angle and length of the baffle by taking the minimum value of the response variable as a target; and 7, verifying the optimal angle and length of the baffle plate, and selecting the most reasonable baffle plate structure. According to the method, the angle and the length of the baffle plate are continuously changed, the flow standard deviation of each air outlet and the system pressure drop are taken as dependent variables, and the minimum value of the response variable is taken as a seeking target, so that the baffle plate structure parameter enabling the flow difference of the air outlets to be minimum and the flow resistance of the pipeline to be minimum can be accurately found.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy storage systems, and in particular relates to a parameter optimization design method for air duct baffles in a battery compartment of an energy storage container. Background Art

[0002] In the field of energy storage containers, air-cooled thermal management systems have become the main temperature control solution due to their simple structure and low cost. Ensuring the uniformity of air outlets at the air conditioning pipes in the battery compartment is the key to ensuring the temperature balance inside the system. The current solution is to set baffles at each air outlet and use CFD technology to adjust the length and angle parameters of each outlet baffle to verify the flow uniformity. Due to the large number of variables and the large variable adjustment range, each parameter adjustment requires remodeling, mesh division, calculation and other processes, which involves a lot of repetitive work and takes a long time to calculate. The manually selected baffle parameters are not necessarily the best method, and the work efficiency is low. Summary of the invention

[0003] In order to solve the technical problems existing in the prior art, the purpose of the present invention is to provide a method for optimizing the design of parameters of air duct baffles in a battery compartment of an energy storage container.

[0004] In order to achieve the above purpose and the above technical effect, the technical solution adopted by the present invention is:

[0005] A method for optimizing the design of parameters of an air duct baffle in a battery compartment of an energy storage container comprises the following steps:

[0006] Step 1: Build a battery compartment model and set the baffle, with the length and angle of the baffle as driving parameters;

[0007] Step 2: Grid division and fluid domain extraction;

[0008] Step 3: Set boundary conditions and solve parameters in the solver;

[0009] Step 4: Select the experimental design type and perform multi-condition simulation;

[0010] Step 5: Determine the response variable, set the response surface type, and fit the response surface model equation;

[0011] Step 6: Set the minimum value of the response variable as the target and seek the optimal angle and length of the baffle;

[0012] Step 7: Verify the optimal angle and length of the baffle and select the most reasonable baffle structure.

[0013] Furthermore, in step 1, the battery compartment model includes: an air inlet duct, a battery compartment and a return air duct, the return air duct is located at the upper part of the battery compartment, the air inlet duct is located at the lower part of the battery compartment, the air inlet duct has a total of N air outlets, except for one air outlet at the tail, baffles are respectively arranged downstream of the remaining N-1 air outlets, the length and angle of each baffle are adjustable, and the length and horizontal plane angle of the baffle are used as driving parameters, with a total of (N-1)×2 independent variables.

[0014] Furthermore, in step 1, after the battery compartment model is constructed, the chamfers are optimized, extra edges, extra points, and small faces are removed, interference is removed from common areas, and finally the model structure is shared.

[0015] Furthermore, in step 2, the steps of meshing and fluid domain extraction include:

[0016] (21) Verify grid independence and determine the minimum grid size and the total number of grids;

[0017] (22) Divide the surface mesh size to ensure that the surface mesh distortion rate quality is below 0.7;

[0018] (23) Close the model and extract the fluid domain;

[0019] (24) Determine the boundary layer and divide the volume mesh to ensure that the orthogonality quality of the volume mesh is above 0.15.

[0020] Furthermore, in step (21), the steps of verifying the independence of the grid and determining the minimum grid size and the total number of grids include:

[0021] The same set of baffle length and angle parameters were selected for testing. The default maximum grid size was 1 / 10 of the width of the air inlet duct, and the size was Z1. The total number of model grids was set by continuously changing the minimum grid size. The total number of grids was used as the independent variable and the model pressure drop was used as the dependent variable. A line graph was drawn to test the sensitivity of the model pressure drop to the number of grids. When the total number of grids was small, the pressure drop would change with the number of grids, and when the number of grids reached a certain amount, the pressure drop value remained basically unchanged. Therefore, the number of grids corresponding to the turning point in the stable value was selected as the total number of grids, and the model grid was divided into the minimum grid size Z2 and the maximum size Z1 corresponding to the total number of grids.

[0022] Furthermore, in step 3, the steps of setting boundary conditions and solving parameters in the solver include:

[0023] (31) The flow rate Qi of each air outlet is used as the output parameter, i is 1, 2, 3…n, where n is the number of air outlets. The residual curve is used to determine whether the simulation converges. The standard deviation P1 of the flow rate of each air outlet is used to determine whether the flow rate is evenly distributed. At the same time, the system pressure drop ΔP is calculated as another output parameter.

[0024] (32) Select appropriate turbulence equations;

[0025] (33) Select the solution method and discretization equation;

[0026] (34) Setting the convergence criteria and the total number of iteration steps;

[0027] The total number of iteration steps set must be greater than the number of iteration steps required for model convergence; check the residual curve and the flow rate of each outlet during calculation to ensure that the residual is within 10 at the end of the calculation. -3 The values ​​below and at the outlet flow monitoring points are stable.

[0028] Furthermore, in step (31), the flow standard deviation P1 is calculated as follows:

[0029]

[0030] The system pressure drop ΔP is calculated as follows:

[0031]

[0032] Where A is the area of ​​the air inlet duct inlet surface, is the average static pressure on the inlet surface of the air inlet duct, is the static pressure at the return air duct outlet, and P is the static pressure value.

[0033] Furthermore, in step (32), the turbulence equation adopts the SST k-omega equation, which is a two-equation turbulence model. It combines the advantages of the k-epsilon model and the k-omega model, and can provide accurate and reliable turbulence prediction results in both the boundary layer region and the free layer region. The equations to be solved are divided into the K equation and the Omega equation.

[0034] The K equation is used to describe the change of turbulent energy, and its governing equation is as follows:

[0035]

[0036] Where ρ is the fluid density, k is the turbulent kinetic energy, and u j is the velocity component, μ is the dynamic viscosity, σ k is the correction factor, μ t is the turbulent dynamic viscosity, P kis the turbulence energy generation term, ρε is the turbulence dissipation rate, t is the time variable, x j The three directional components are xyz;

[0037] The Omega equation is used to describe the change of turbulent dissipation rate, and its governing equation is as follows:

[0038]

[0039] Where ω is the turbulent dissipation rate, σ ω is the correction factor, P ω is the term for turbulent dissipation rate, C lim is the limiting factor.

[0040] Furthermore, in step 5, the response variable is the dimensionless weighted value of flow rate and pressure drop, and the calculation formula is:

[0041]

[0042] in, They are the average value of the flow standard deviation P1 and the average value of the system pressure drop ΔP of the test data respectively. W1 and W2 are weight factors, which are 0.8 and 0.2 respectively.

[0043] Furthermore, in step 5, the response surface model equation is fitted according to the test results, and the formula is as follows:

[0044]

[0045] Among them, x i 、x i x j 、x i 2 are the linear terms, cross terms, and square terms in 2(N-1) independent variables; β0, β i , β ij , β ii are the coefficients of the constant term, linear term, cross term, and square term respectively; ε is the error between the response surface equation and the actual data;

[0046] Error analysis is performed on the response surface model to verify the rationality of the model; error analysis is an indicator of the accuracy of the response surface, which is represented by the root mean square error. The error between the response surface model Di and the actual simulation data Ti is compared. If the root mean square error is less than 5%, it is considered that the accuracy of the response surface model meets the requirements.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention discloses a parameter optimization design method for air duct baffles in a battery compartment of an energy storage container. By modeling and continuously changing the angle and length of the baffles, the standard deviation of the air outlet flow and the system pressure drop are used as dependent variables, and the minimum value of the response variable is sought as a target. The baffle structural parameters that minimize the difference in air outlet flow and minimize the flow resistance of the pipeline can be accurately found. The method has high working efficiency and is suitable for industrial promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of the present invention;

[0050] Figure 2 A model diagram of a battery compartment of the present invention;

[0051] Figure 3 It is a schematic diagram of the baffle structure of the present invention;

[0052] Figure 4 This is a grid independence verification diagram of the present invention. DETAILED DESCRIPTION

[0053] The present invention is described in detail below so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0054] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or critical elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0055] like Figure 1-4 As shown, a parameter optimization design method for the air duct baffle of the battery compartment of an energy storage container includes the following steps:

[0056] Step 1: Build a battery compartment model and set the baffle, with the length and angle of the baffle as driving parameters;

[0057] Step 2: Grid division and fluid domain extraction;

[0058] Step 3: Set boundary conditions and solve parameters in the solver;

[0059] Step 4: Select the experimental design type and perform multi-condition simulation;

[0060] Step 5: Determine the response variable, set the response surface type, and fit the response surface model equation;

[0061] Step 6: Set the minimum value of the response variable as the target and seek the optimal angle and length of the baffle;

[0062] Step 7: Verify the optimal angle and length of the baffle and select the most reasonable baffle structure.

[0063] In step 1, the battery compartment model structure includes: a battery compartment 1, a return air duct 2 and an air inlet duct 3. The return air duct 2 is located at the upper part of the battery compartment 1, and the air inlet duct 3 is located at the lower part of the battery compartment 1. The air inlet duct 3 has a total of N air outlets. Except for one air outlet at the tail, baffles are respectively arranged downstream of the remaining N-1 air outlets. One end of the baffle is fixed to the wall. The length and horizontal plane angle of each baffle are adjustable. The length and horizontal plane angle of the baffle are used as driving parameters, and there are a total of (N-1)×2 independent variables.

[0064] In step 1, after the battery compartment model is built, the model needs to be processed. During the processing, the chamfers are optimized, extra edges, extra points, and small faces are removed, interference is removed from common areas, and finally the model structure is shared.

[0065] In step 2, the steps of meshing and fluid domain extraction include:

[0066] (21) Verify grid independence and determine the minimum grid size and the total number of grids;

[0067] (22) Divide the surface mesh size to ensure that the surface mesh distortion rate quality is below 0.7;

[0068] (23) Close the model and extract the fluid domain;

[0069] (24) Determine the boundary layer and divide the volume mesh to ensure that the orthogonality quality of the volume mesh is above 0.15.

[0070] In step (21), the steps of verifying the independence of the grid and determining the minimum grid size and the total number of grids include:

[0071] The same set of baffle length and angle parameters were selected for testing. The default maximum grid size was 1 / 10 of the width of the air inlet duct, and the size was Z1. The total number of model grids was set by continuously changing the minimum grid size. The total number of grids was used as the independent variable and the model pressure drop was used as the dependent variable. A line graph was drawn to test the sensitivity of the model pressure drop to the number of grids, such as Figure 4 As shown in the figure, when the total number of grids is small, the pressure drop will change with the number of grids, and when the number of grids reaches a certain amount, the pressure drop value remains basically unchanged. Therefore, the number of grids corresponding to the turning point in the stable value is selected as the total number of grids, and the model grid is divided into the minimum grid size Z2 and the maximum size Z1 corresponding to the total number of grids.

[0072] In step 3, the steps of setting boundary conditions and solving parameters in the solver include:

[0073] (31) The flow rate Qi of each air outlet is used as the output parameter, i is 1, 2, 3…n, where n is the number of air outlets. The residual curve is used to determine whether the simulation converges. The standard deviation P1 of the flow rate of each air outlet is used to determine whether the flow rate is evenly distributed. At the same time, the system pressure drop ΔP is calculated as another output parameter.

[0074] (32) Select appropriate turbulence equations;

[0075] (33) Select the solution method and discretization equation;

[0076] (34) Setting the convergence criteria and the total number of iteration steps;

[0077] The total number of iteration steps set must be greater than the number of iteration steps required for model convergence; check the residual curve and the flow rate of each outlet during calculation to ensure that the residual is within 10 at the end of the calculation. -3 The values ​​below and at the outlet flow monitoring points are stable.

[0078] In step (31), the flow standard deviation P1 is calculated as follows:

[0079]

[0080] The system pressure drop ΔP is calculated as follows:

[0081]

[0082] Where A is the area of ​​the air inlet duct inlet surface, is the average static pressure on the inlet surface of the air inlet duct, is the static pressure at the return air duct outlet, and P is the static pressure value.

[0083] In step (32), the turbulence equation preferably uses the SST k-omega equation. The SST k-omega equation is a two-equation turbulence model that combines the advantages of the k-epsilon model and the k-omega model. It can provide accurate and reliable turbulence prediction results in both the boundary layer region and the free layer region. The equations to be solved are divided into the K equation and the Omega equation.

[0084] The K equation is used to describe the change of turbulent energy, and its governing equation is as follows:

[0085]

[0086] Where ρ is the fluid density, k is the turbulent kinetic energy, and u j is the velocity component, μ is the dynamic viscosity, σ k is the correction factor, μ tis the turbulent dynamic viscosity, P k is the turbulence energy generation term, ρε is the turbulence dissipation rate, t is the time variable, x j The three directional components are xyz;

[0087] The Omega equation is used to describe the change of turbulent dissipation rate, and its governing equation is as follows:

[0088]

[0089] Where ω is the turbulent dissipation rate, σ ω is the correction factor, P ω is the term for turbulent dissipation rate, C lim is the limiting factor.

[0090] In step 5, the response variable is the dimensionless weighted value of flow rate and pressure drop, and the calculation formula is:

[0091]

[0092] in, They are the average value of the flow standard deviation P1 and the average value of the system pressure drop ΔP of the test data respectively. W1 and W2 are weight factors, which are 0.8 and 0.2 respectively.

[0093] In step 5, the response surface model equation is fitted according to the test results, and the formula is as follows:

[0094]

[0095] Among them, x i 、x i x j 、x i 2 are the linear terms, cross terms, and square terms in 2(N-1) independent variables; β0, β i , β ij , β ii are the coefficients of the constant term, linear term, cross term, and square term respectively; ε is the error between the response surface equation and the actual data.

[0096] Error analysis is performed on the response surface model to verify the rationality of the model; error analysis is an indicator of the accuracy of the response surface, which is represented by the root mean square error. The error between the response surface model Di and the actual simulation data Ti is compared. If the root mean square error is less than 5%, it is considered that the accuracy of the response surface model meets the requirements.

[0097] Example 1

[0098] A method for optimizing the design of parameters of an air duct baffle in a battery compartment of an energy storage container comprises the following steps:

[0099] Step 1: Build and process the battery compartment model, and set the length and angle of the baffle as the driving parameters:

[0100] The battery compartment model structure includes: a battery compartment 1, a return air duct 2 and an inlet air duct 3, wherein the return air duct 2 is located at the upper part of the battery compartment 1, and the inlet air duct 3 is located at the lower part of the battery compartment 1. The inlet air duct 3 has a total of N = 5 air outlets. Except for one air outlet at the tail, baffles are respectively set downstream of the remaining N-1 = 4 air outlets. One end of the baffle is fixed to the wall. The length and horizontal angle of the baffles of these 4 air outlets can be changed. The length and horizontal angle of the baffle are used as driving parameters, and there are a total of 8 independent variables;

[0101] During the model processing, chamfers are optimized, extra edges, extra points, and small faces are removed, interference is performed to remove common areas, and finally the model structure is shared.

[0102] Step 2: Meshing and fluid domain extraction:

[0103] (21) Verify grid independence and determine the minimum grid size and the total number of grids;

[0104] The same set of baffle length and angle parameters were selected for testing. The default maximum grid size was 1 / 10 of the air inlet duct width, and the size was Z1. The total number of model grids was set by continuously changing the minimum grid size, and the total number of grids was used as the independent variable and the model pressure drop as the dependent variable. A line graph was drawn to test the sensitivity of the model pressure drop to the number of grids, such as Figure 4 As shown, when the total number of grids is small, the pressure drop will change with the number of grids, and when the number of grids reaches a certain amount, the pressure drop value remains basically unchanged. Therefore, the number of grids corresponding to the turning point in the stable value is selected as the total number of grids, and the model grid is divided according to the minimum grid size Z2 and the maximum size Z1 corresponding to the total number of grids. In this embodiment, the total number of grids is 3000W, and the corresponding minimum grid size is 2mm;

[0105] (22) Divide the surface mesh size to ensure that the surface mesh distortion rate quality is below 0.7;

[0106] It is recommended to use tetrahedral mesh for surface mesh, which can capture model details, ensure calculation speed and is more suitable for complex models such as energy storage container systems;

[0107] (23) Close the model and extract the fluid domain;

[0108] (24) Determine the boundary layer and divide the volume mesh to ensure that the volume mesh orthogonality quality is above 0.15;

[0109] Ensure that the boundary layer has more than three layers of mesh, and the boundary layer mesh is preferably a prismatic layer. A denser mesh in the area where the fluid is close to the surface of the object can provide a more accurate and detailed flow distribution, and can better capture the changes in the fluid velocity planing surface and the fluid viscosity effect. Ensuring the quality of the surface mesh and the volume mesh can prevent divergence due to poor mesh quality when changing the baffle structure parameters.

[0110] Step 3: Set boundary conditions and solution parameters in the solver

[0111] (31) Set a velocity inlet or mass flow inlet on the inlet surface of the air inlet duct, and set a pressure outlet on the outlet surface of the return air duct. Take the flow rate of each outlet (the flow rate is recorded as Q1, Q2, ... Qi, ... Qn, i is 1, 2, 3 ... n, and n is the number of outlets) as the output parameter, combined with the residual curve to determine whether the simulation converges, and use the standard deviation P1 of the flow rate of each outlet to determine whether the flow rate is evenly distributed. The calculation method of P1 is as follows:

[0112]

[0113] At the same time, the system pressure drop ΔP is set as another output parameter, which is the average static pressure on the inlet surface of the air inlet duct Subtract the return air duct outlet static pressure The formula is:

[0114]

[0115] Where A is the area of ​​the inlet surface of the air inlet duct;

[0116] (32) Select a suitable turbulence equation. The present embodiment preferably uses the SST k-omega equation. The SST k-omega equation is a two-equation turbulence model that combines the advantages of the k-epsilon model and the k-omega model. It can provide accurate and reliable turbulence prediction results in both the boundary layer region and the free layer region. The equations to be solved can be divided into the K equation and the Omega equation;

[0117] The K equation is used to describe the change of turbulent energy, and its governing equation is as follows:

[0118]

[0119] Where ρ is the fluid density, k is the turbulent kinetic energy, and u j is the velocity component, μ is the dynamic viscosity, σ k is the correction factor, μ t is the turbulent dynamic viscosity, P k is the turbulence energy generation term, ρε is the turbulence dissipation rate, t is the time variable, x j The three directional components are xyz;

[0120] The Omega equation is used to describe the change of turbulent dissipation rate, and its governing equation is as follows:

[0121]

[0122] Where ω is the turbulent dissipation rate, σ ω is the correction factor, P ω is the term for turbulent dissipation rate, C lim is the limiting factor;

[0123] (33) Select the solution method and discretization equation;

[0124] (34) Setting the convergence criteria and the total number of iteration steps;

[0125] The total number of steps set must be greater than the number of iterations required for model convergence. Check the residual curve and the flow rate of each outlet during calculation to ensure that the residual is within 10 at the end of the calculation. -3 The values ​​below and at the outlet flow monitoring points are stable.

[0126] Step 4: Select the experimental design type and perform multi-condition simulation;

[0127] Determine the level value of each factor. In this embodiment, the baffle angles suitable for processing are 15°, 30°, 45°, 60°, and 75°. The baffle lengths are 80mm, 90mm, 100mm, 115mm, 120mm, 130mm, 140mm, and 150mm. Select the central composite design method (CCD) or Box-Behnken experimental design (BBD) to perform N groups of multi-condition parameter simulations. In this embodiment, the BBD experimental design is used because some points of the CCD design exceed the original level and cannot be achieved during actual processing. When the factors are the same, the BBD design requires fewer experiments, which can save a lot of computing resources. In this embodiment, a total of 65 groups of experimental conditions are set, as shown in Table 1:

[0128] Table 1

[0129]

[0130]

[0131] Step 5: Determine the response variable, set the response surface type, and fit the response surface model equation. The response variable is the weighted value of the flow rate and pressure drop after dimensionlessness. The calculation formula is:

[0132]

[0133] in, They are the average value of the flow standard deviation P1 and the average value of the system pressure drop ΔP of n groups of test data, respectively. To unify the dimensions, W1 and W2 are weight factors, which are 0.8 and 0.2 respectively;

[0134] The response surface model equation is fitted according to the test results, and the second-order response surface model is preferably used. The formula is as follows:

[0135]

[0136] The response surface model is subjected to error analysis to verify the rationality of the model. Error analysis is an indicator of the accuracy of the response surface, and this embodiment uses the root mean square error to characterize it. Comparing the error between the response surface model Di and the actual simulation data Ti, if the root mean square error is less than 5%, it can be considered that the accuracy of the response surface model meets the requirements. The root mean square error formula is as follows:

[0137]

[0138] Step 6: Set the minimum value of the response variable as the target and find the optimal angle and length of the baffle

[0139] The minimum value of the response variable f(x) is used as the goal to find the optimal baffle structural parameters (optimal angle and length). According to the design requirements of the air inlet duct, all baffle angles and lengths in step 4 are set not to be greater than or less than certain values. In this embodiment, the angles are between 15° and 75°, and the lengths are between 80 mm and 150 mm. According to the fitted response surface model equation, optimization is performed to obtain multiple sets of optimal baffle structural parameters.

[0140] Step 7: Verify the optimal angle and length of the baffle and select the most reasonable baffle structure

[0141] Verify the optimal baffle length and angle. Re-model the baffle structural parameters obtained in step 6, and compare the actual simulation result with the fitting value. If the relative error is less than 5%, the model is considered reasonable, and the baffle structural parameters obtained by fitting are the optimal baffle parameters. In this embodiment, the optimal parameters obtained by fitting and the verification information are shown in Table 2 below:

[0142] Table 2

[0143]

[0144]

[0145] Therefore, the parameters of point 1, the baffle angle and length are (30°, 30°, 75°, 60°) and (110mm, 150mm, 100mm, 150mm), which are the optimal baffle structures.

[0146] Parts or structures not specifically described in the present invention may adopt existing technologies or existing products and will not be described in detail here.

[0147] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the design of air duct baffle parameters in a battery compartment of an energy storage container, characterized in that: The following steps are involved: Step 1: Build a battery compartment model and set the baffle, with the length and angle of the baffle as driving parameters; Step 2: Grid division and fluid domain extraction; Step 3: Set boundary conditions and solve parameters in the solver; Step 4: Select the experimental design type and perform multi-condition simulation; Step 5: Determine the response variable, set the response surface type, and fit the response surface model equation; Step 6: Set the minimum value of the response variable as the target and seek the optimal angle and length of the baffle; Step 7: Verify the optimal angle and length of the baffle and select the most reasonable baffle structure.

2. The method for optimizing the parameters of the wind duct baffle of the battery compartment of an energy storage container according to claim 1 is characterized in that: In step 1, the battery compartment model includes: an air inlet duct, a battery compartment and a return air duct. The return air duct is located at the upper part of the battery compartment, and the air inlet duct is located at the lower part of the battery compartment. The air inlet duct has a total of N air outlets. Except for one air outlet at the rear, baffles are respectively set downstream of the remaining N-1 air outlets. The length and angle of each baffle are adjustable. The length and horizontal plane angle of the baffle are used as driving parameters, and there are a total of (N-1)×2 independent variables.

3. The method for optimizing the parameters of the wind duct baffle of the battery compartment of an energy storage container according to claim 1 is characterized in that: In step 1, after the battery compartment model is constructed, the chamfers are optimized, extra edges, extra points, and small faces are removed, interference is removed from common areas, and finally the model structure is shared.

4. The method for optimizing the parameters of the wind duct baffle of the battery compartment of an energy storage container according to claim 1 is characterized in that: In step 2, the steps of meshing and fluid domain extraction include: (21) Verify grid independence and determine the minimum grid size and the total number of grids; (22) Divide the surface mesh size to ensure that the surface mesh distortion rate quality is below 0.7; (23) Close the model and extract the fluid domain; (24) Determine the boundary layer and divide the volume mesh to ensure that the orthogonality quality of the volume mesh is above 0.

15.

5. The method for optimizing the design of parameters of the air duct baffle of the battery compartment of an energy storage container according to claim 4 is characterized in that: In step (21), the steps of verifying the independence of the grid and determining the minimum grid size and the total number of grids include: The same set of baffle length and angle parameters were selected for testing. The default maximum grid size was 1 / 10 of the width of the air inlet duct, and the size was Z1. The total number of model grids was set by continuously changing the minimum grid size. The total number of grids was used as the independent variable and the model pressure drop was used as the dependent variable. A line graph was drawn to test the sensitivity of the model pressure drop to the number of grids. When the total number of grids was small, the pressure drop would change with the number of grids, and when the number of grids reached a certain amount, the pressure drop value remained basically unchanged. Therefore, the number of grids corresponding to the turning point in the stable value was selected as the total number of grids, and the model grid was divided into the minimum grid size Z2 and the maximum size Z1 corresponding to the total number of grids.

6. The method for optimizing the design of parameters of the air duct baffle of the battery compartment of an energy storage container according to claim 1, characterized in that: In step 3, the steps of setting boundary conditions and solving parameters in the solver include: (31) The flow rate Qi of each air outlet is used as the output parameter, i is 1, 2, 3…n, where n is the number of air outlets. The residual curve is used to determine whether the simulation converges. The standard deviation P1 of the flow rate of each air outlet is used to determine whether the flow rate is evenly distributed. At the same time, the system pressure drop ΔP is calculated as another output parameter. (32) Select appropriate turbulence equations; (33) Select the solution method and discretization equation; (34) Setting the convergence criteria and the total number of iteration steps; The total number of iteration steps set must be greater than the number of iteration steps required for model convergence; check the residual curve and the flow rate of each outlet during calculation to ensure that the residual is within 10 at the end of the calculation. -3 The values ​​below and at the outlet flow monitoring points are stable.

7. The method for optimizing the parameters of the wind duct baffle of the battery compartment of an energy storage container according to claim 6 is characterized in that: In step (31), the flow standard deviation P1 is calculated as: The system pressure drop ΔP is calculated as follows: Where A is the area of ​​the air inlet duct inlet surface, is the average static pressure on the inlet surface of the air inlet duct, is the static pressure at the return air duct outlet, and P is the static pressure value.

8. The method for optimizing the design of parameters of the air duct baffle of the battery compartment of an energy storage container according to claim 6, characterized in that: In step (32), the turbulence equation adopts the SST k-omega equation. The SST k-omega equation is a two-equation turbulence model. It combines the advantages of the k-epsilon model and the k-omega model, and can provide accurate and reliable turbulence prediction results in both the boundary layer region and the free layer region. The equations to be solved are divided into the K equation and the Omega equation. The K equation is used to describe the change of turbulent energy, and its governing equation is as follows: Where ρ is the fluid density, k is the turbulent kinetic energy, and u j is the velocity component, μ is the dynamic viscosity, σ k is the correction factor, μ t is the turbulent dynamic viscosity, P k is the turbulence energy generation term, ρε is the turbulence dissipation rate, t is the time variable, x j The three directional components are xyz; The Omega equation is used to describe the change of turbulent dissipation rate, and its governing equation is as follows: Where ω is the turbulent dissipation rate, σ ω is the correction factor, P ω is the term for turbulent dissipation rate, C lim is the limiting factor.

9. The method for optimizing the design of parameters of the air duct baffle of the battery compartment of an energy storage container according to claim 1, characterized in that: In step 5, the response variable is the dimensionless weighted value of flow rate and pressure drop, and the calculation formula is: in, They are the average value of the flow standard deviation P1 and the average value of the system pressure drop ΔP of the test data respectively. W1 and W2 are weight factors, which are 0.8 and 0.2 respectively.

10. The method for optimizing the design of parameters of the air duct baffle of the battery compartment of an energy storage container according to claim 1, characterized in that: In step 5, the response surface model equation is fitted according to the test results, and the formula is as follows: Among them, x i 、x i x j 、x i 2 are the linear terms, cross terms, and square terms in 2(N-1) independent variables; β0, β i , β ij , β ii are the coefficients of the constant term, linear term, cross term, and square term respectively; ε is the error between the response surface equation and the actual data; Error analysis is performed on the response surface model to verify the rationality of the model; error analysis is an indicator of the accuracy of the response surface, which is characterized by the root mean square error. The error between the response surface model Di and the actual simulation data Ti is compared. If the root mean square error is less than 5%, it is considered that the accuracy of the response surface model meets the requirements.

Images