Optimization method for heat dissipation of automobile power battery pack based on liquid cooling plate

Abstract

The invention discloses an optimization method for heat dissipation of an automobile power battery pack based on a liquid cooling plate. The optimization method comprises the following steps: acquiring a battery pack three-dimensional model comprising the liquid cooling plate and a battery module; establishing a finite element model of the battery pack, and simulating the battery pack; obtaining a design variable, and determining an optimization target; reconstructing a parameterized geometric model of the liquid cooling plate structure according to the design variables; sequentially changing the value of each design variable, re-establishing the finite element model of the battery pack, and performing corresponding simulation calculation; selecting a preset number of design variables based on the influence of each design variable on the simulation result, determining the value range of the selected design variables, and extracting multiple groups of sample points; establishing a corresponding battery pack parameterized geometric model and a finite element model according to the sample points, and performing simulation calculation; establishing a target function of a design variable and an optimization target according to the calculation data; and carrying out optimization processing to obtain an optimization result. According to the invention, the value of each design factor can be accurately optimized, and the heat dissipation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of heat dissipation of automotive power battery packs, and particularly relates to an optimization method for heat dissipation of automotive power battery packs based on liquid cooling plates. Background Art

[0002] At present, the thermal management technologies of electric vehicle battery packs are mainly divided into three types: natural convection heat dissipation, air-cooled heat dissipation, and liquid-cooled heat dissipation. Due to poor heat dissipation effects, natural convection heat dissipation and air-cooled heat dissipation cannot effectively solve the excessive heat generated during high-power discharge, especially in high-power battery packs, and there are problems such as high noise and high energy consumption. Therefore, the liquid-cooled heat dissipation technology has gradually become the mainstream solution due to its superior heat conduction performance.

[0003] The liquid-cooled system allows the coolant to flow inside the battery pack to absorb the heat released by the battery and discharge the heat through a heat dissipation device. Its advantage is that it can maintain the uniformity of the battery temperature, avoid local overheating, and thus improve the battery performance and extend the service life. However, the liquid-cooled system still faces several challenges in practical applications, especially in the design and optimization of liquid cooling plates.

[0004] The liquid cooling plate is a key component in the liquid-cooled system, and its design directly affects the heat dissipation effect. The current designs of liquid cooling plates are mostly based on experience, lacking precise simulation and analysis of the thermal distribution of the battery pack, often resulting in uneven heat dissipation in some areas, and even local overheating, which affects the battery safety.

[0005] The thermal contact surface between the liquid cooling plate and the battery cell is also an important issue in the design. Traditional liquid cooling plate designs often cannot ensure good contact between the liquid cooling plate and the battery cell, resulting in low heat conduction efficiency. Therefore, how to optimize the contact method between the liquid cooling plate and the battery cell to improve the heat conduction efficiency is the key to improving the performance of the liquid-cooled heat dissipation system.

[0006] In addition, the complexity and cost of the liquid-cooled system are also factors limiting its widespread application. The liquid-cooled system usually requires additional components such as coolant, pumps, and pipes, which increase the volume, weight, and cost of the system. In the design of electric vehicles, lightweight and cost control are very important goals. Therefore, how to simplify the structure and reduce the cost while improving the heat dissipation performance is the core task of liquid-cooled technology optimization.

[0007] There has been an increasing amount of research on liquid-cooled plate heat dissipation systems both at home and abroad. For example, K. Monika et al. designed and optimized the flow channels of a liquid-cooled plate that introduced the concept of a Tesla valve. By using the method of controlling variables to change the design factors, they explored the influence of each variable on the performance of the liquid-cooled plate, providing valuable experience for experts and scholars in this field. However, the limitation is that it only considers the influence of a single variable and lacks the correlation between variables, resulting in low accuracy of the optimization results. (Monika K, Chakraborty C, Roy S, et al. A numerical analysis on multi-stage Tesla valve based cold plate for cooling of pouch type Li-ion batteries[J]. International Journal of Heat and Mass Transfer, 177(2021)121560.) Summary of the Invention

[0008] In order to solve at least one of the problems existing in the prior art, the present invention provides an optimization method for the heat dissipation of an automotive power battery pack based on a liquid-cooled plate, which can select appropriate design variables affecting the heat dissipation of the battery pack under certain heat dissipation conditions to achieve the best battery heat dissipation effect.

[0009] To achieve the purpose of the present invention, an optimization method for the heat dissipation of an automotive power battery pack based on a liquid-cooled plate provided by the present invention includes the following steps:

[0010] Obtain a three-dimensional model of the battery pack including the liquid-cooled plate and the battery module;

[0011] Establish a finite element model of the battery pack including the liquid-cooled plate and the battery module, and perform simulation on the battery pack;

[0012] Obtain design variables and determine the optimization objective;

[0013] Reconstruct a parametric geometric model of the liquid-cooled plate structure according to the design variables;

[0014] Adopt the method of controlling variables, sequentially change the values of each design variable, re-establish a finite element model of the battery pack including the liquid-cooled plate and the battery module, and perform corresponding simulations;

[0015] Based on the influence of each design variable on the simulation results, select a preset number of design variables based on the influence on the simulation results, determine the value range of the selected design variables, and extract multiple groups of sample points;

[0016] Establish corresponding parametric geometric models and finite element models of the battery pack according to the sample points, perform simulation calculations, and obtain simulation calculation results;

[0017] Establish an objective function for design variables and optimization objectives based on the simulation calculation results;

[0018] Optimize the selected design variables based on the objective function and the overall optimization objective to obtain the optimization results of the design variables.

[0019] Furthermore, conduct a heat dissipation condition experiment on the battery pack to obtain experimental data; compare the simulation results of the finite element model of the battery pack including the liquid cooling plate and the battery module with the experimental data to make the error between the two within a preset range. Otherwise, change the parameters of the finite element model of the battery pack including the liquid cooling plate and the battery module and re - conduct the simulation calculation.

[0020] Furthermore, the design variables are selected from the height and width of the liquid cooling plate flow channel, the thickness of the liquid cooling plate, the coolant flow rate and velocity, the coolant temperature, the inclination angle of the secondary flow channel, the number of main flow channels, the number of secondary flow channels, and the contact method between the liquid cooling plate and the battery cells.

[0021] Furthermore, after selecting a preset number of design variables, use the experimental design method to extract multiple groups of sample points within the value range of the design variables.

[0022] Furthermore, the experimental design method is one of the central composite design method and the fractional factorial experimental design method.

[0023] For the value of each design variable, a three - level value is adopted. If the number of design variables is greater than 3, the experimental design method adopts the central composite design method (1 / 2 fractional factorial design); if the number of design variables is less than or equal to 3, the experimental design method adopts the central composite design method (full factorial experimental design).

[0024] Furthermore, the expression of the objective function is:

[0025]

[0026] In the formula, Y is the optimization objective, x i is the design variable, k i and k mn are coefficients, n is the number of design variables, m ∈ n, k ∈ n, and m ≠ k.

[0027] Furthermore, solve the objective function through a mathematical regression method.

[0028] Furthermore, the expression of the overall optimization objective is:

[0029]

[0030] In the formula, Z is the overall objective, Y i is the objective function of the i - th optimization objective, N is the number of optimization objectives, Ki The weighting coefficient for the optimization objective. Among them, the value of the weighting coefficient is determined based on the importance of the optimization objective.

[0031] The present invention also provides a computer device.

[0032] The present invention also provides a computer-readable storage medium.

[0033] Compared with the prior art, the present invention has at least the following advantages:

[0034] 1) The optimization method for the heat dissipation of the automotive power battery pack based on the liquid cooling plate can accurately optimize the values of each design variable and improve the heat dissipation efficiency.

[0035] 2) The optimization method for the heat dissipation of the automotive power battery pack based on the liquid cooling plate can effectively reduce the production cycle, achieve the optimal effect before mass production of the product, reduce the trial-and-error rate and lower the cost.

[0036] 3) The optimization method for the heat dissipation of the automotive power battery pack based on the liquid cooling plate can optimize the structure of the liquid cooling system while ensuring the heat dissipation effect, reduce unnecessary weight, and meet the lightweight requirements of electric vehicles.

[0037] 4) The optimization method for the heat dissipation of the automotive power battery pack based on the liquid cooling plate can reduce the risk of battery overheating through precise temperature control, and thus improve the safety of the battery pack. Brief Description of the Drawings

[0038] Figure 1 is a flowchart of an optimization method for the heat dissipation of an automotive power battery pack based on a liquid cooling plate provided by an embodiment of the present invention.

[0039] Figure 2 is a schematic diagram of a simplified three-dimensional model of a battery pack in an embodiment of the present invention.

[0040] Figure 3 is a schematic diagram of a liquid cooling plate structure in an embodiment of the present invention.

[0041] Figure 4 is a schematic diagram of a liquid cooling plate flow channel in an embodiment of the present invention.

[0042] Figure 5 is a cross-sectional view of a flow channel in an embodiment of the present invention. Detailed Description of the Embodiments

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts are within the scope of protection of the present invention.

[0044] As Figure 1 shown, an optimization method for the heat dissipation of an automotive power battery pack based on a liquid cooling plate provided by the present invention includes the following steps:

[0045] Step 1: Obtain the three-dimensional model of the battery pack.

[0046] In the original three-dimensional model file of the battery pack, remove the irrelevant components and retain the main components to obtain a three-dimensional model of the battery pack including the liquid cooling plate and the battery module. (Liquid cooling plate, battery module).

[0047] Step 2: Conduct a heat dissipation condition experiment on the battery pack and record the relevant experimental data.

[0048] The experimental data includes the pressure drop of the liquid cooling plate, the highest temperature of the battery module, and the maximum temperature difference.

[0049] In some embodiments of the present invention, there are many experimental conditions, including single charge and discharge experiments of the battery pack, cyclic charge and discharge experiments of the battery pack, and high-temperature discharge experiments of the battery pack, etc. The experiments are selected according to the actual situation.

[0050] Step 3: Establish a finite element model of the battery pack including the liquid cooling plate and the battery module, simulate the battery pack to obtain simulation data, compare the simulation data with the experimental data. If the error between the simulation data and the experimental data is not within the preset range, change the parameters of the finite element model and re-simulate the battery pack.

[0051] In some embodiments of the present invention, in the established finite element model of the battery pack, tetrahedral meshes are used, and the maximum volume collapse rate coefficient is set to 0.9.

[0052] In some embodiments of the present invention, the error between the experimental data and the simulation data is less than 5%.

[0053] In some embodiments of the present invention, the parameters of the finite element model can be the mesh size, the quality of the overall mesh, etc.

[0054] In some embodiments of the present invention, the obtained simulation data includes the pressure drop of the liquid cooling plate, the highest temperature of the battery module, and the maximum temperature difference. The simulation data and the experimental data are both recorded in a table, the error between the simulation data and the experimental data is calculated, and a two-dimensional graph can be output for comparison.

[0055] Step 4: Obtain the design variables and determine the optimization objectives.

[0056] The design variables can be: the height and width of the liquid cooling plate flow channel, the thickness of the liquid cooling plate, the coolant flow rate and velocity, the coolant temperature, the inclination angle of the secondary flow channel, the number of main flow channels, the number of secondary flow channels, the contact method between the liquid cooling plate and the battery cell, etc. The selection of the design variables depends on the actual situation.

[0057] Among them, by using the contact method between the liquid cooling plate and the battery cell as the optimization variable, the optimization of the contact method between the liquid cooling plate and the battery cell can be realized, so as to improve the heat conduction efficiency.

[0058] Taking the size of the liquid cooling plate as the optimization variable, the optimal size of the liquid cooling plate can finally be obtained, so that unnecessary materials can be reduced, thereby achieving the effects of weight reduction and cost control.

[0059] In some embodiments of the present invention, the optimization objectives are the pressure drop of the liquid cooling plate, the highest temperature of the battery module, and the maximum temperature difference.

[0060] Step 5: Reconstruct the parametric geometric model of the liquid cooling plate structure on the 3D design software according to the design variables.

[0061] Step 6: Adopt the method of controlling variables, sequentially change the values of each design variable, re-establish the finite element model of the battery pack and perform the corresponding simulation calculations to obtain the simulation calculation results, including the pressure drop of the liquid cooling plate, the highest temperature of the battery module, and the maximum temperature difference.

[0062] Among them, the parameters of the finite element model of the battery pack established in this step are the same as those of the finite element model in step 3, the tetrahedral mesh is adopted, and the maximum volume collapse rate coefficient is set to 0.9.

[0063] Step 7: Sort the design variables according to the influence on the simulation results from large to small, select the first few design variables with greater influence, and extract multiple groups of sample points within the value range of the selected design variables by using the experimental design method.

[0064] In some embodiments of the present invention, the experimental design method is: for the value of each design variable, three levels of values are adopted. If the number of design variables is greater than 3, the central composite design method (1 / 2 fractional factorial design) is adopted for the experimental design method. If the number of design variables is less than or equal to 3, the central composite design method (full factorial experimental design) is adopted for the experimental design method.

[0065] The steps of the full factorial experimental design are as follows:

[0066] 1. Determine the factors and levels: Assume there are k factors (i.e., design variables) in the experiment, and each factor has several levels (i.e., the values of the design variables). For example, factor A has 2 levels (A1, A2), and factor B has 3 levels (B1, B2, B3).

[0067] 2. List all combinations: All combinations of factor levels will be listed. For the above example, there are a total of 2×3 = 6 combinations, namely (A1, B1), (A1, B2), (A1, B3), (A2, B1), (A2, B2), (A2, B3).

[0068] 3. Conduct the experiment: Conduct the experiment for each combination and record the response values.

[0069] After obtaining the values of each group of design variables and the corresponding response values, they can be substituted into the objective function in step 9, and then the coefficients of the objective function can be obtained by solving the equation.

[0070] The steps of the 1 / 2 fractional factorial design are as follows:

[0071] 1. Determine the factors and levels: First, determine the factors and levels in the experiment.

[0072] 2. Select partial combinations: The 1 / 2 fractional factorial design selects half of the combinations in the full factorial design for the experiment. For example, for a 2^3 (3 factors, each factor has 2 levels) full factorial design, there are a total of 8 combinations, and the 1 / 2 fractional factorial design will only select 4 of them.

[0073] 3. Construct the design matrix: Select partial combinations through construction methods (such as generating words or folding method) to ensure that these combinations can represent the main effects of the full factorial design.

[0074] 4. Conduct the experiment: Conduct the experiment for the selected combinations and record the response values.

[0075] In some embodiments of the present invention, there are 3 selected design variables with greater influence, including the inclination angle of the secondary flow channel, the number of primary flow channels, and the number of secondary flow channels. Since the structure of each liquid cooling plate is different, it can be understood that in other embodiments, the selected design variables are not necessarily the inclination angle of the secondary flow channel, the number of primary flow channels, and the number of secondary flow channels.

[0076] In some embodiments of the present invention, three levels are designed, which are -1, 0, and 1 respectively. For example, if it is analyzed through steps 6 - 7 that there is an optimal value within the range of 70° - 80° for the inclination angle of the secondary flow channel, then the three levels of the inclination angle of the secondary flow channel are 70°, 75°, and 80° respectively.

[0077] Step 8: Establish the corresponding parametric geometric model and finite element model of the battery pack in the 3D design software, perform simulation calculations on each group of sample points, and obtain the simulation calculation data of each group of sample points. The simulation calculation data includes the pressure drop of the liquid cooling plate, the maximum temperature of the battery module, and the maximum temperature difference.

[0078] Step 9: Establish an objective function between the design variables and the optimization objectives according to the simulation calculation data.

[0079] The expression of the objective function is:

[0080]

[0081] In the formula, Y is the optimization objective, x i is the design variable, k i and k mn are coefficients, n is the number of design variables, m ∈ n, k ∈ n, and m ≠ k.

[0082] In some embodiments of the present invention, there are 3 design variables determined by step 7, including the inclination angle of the secondary flow channel, the number of main flow channels, and the number of secondary flow channels. On this basis, the expression of the objective function (i.e., the approximate model in Figure 1 ) is:

[0083] Y = ax1 2 + bx2 2 + cx3 2 + dx1x2 + ex1x3 + fx2x3

[0084] Among them, Y is the optimization objective, x1, x2, and x3 are the design variables: the inclination angle of the secondary flow channel, the number of main flow channels, and the number of secondary flow channels, respectively, and a, b, c, d, e, and f are all coefficients.

[0085] The objective function can be solved by the mathematical regression method. For the battery pack heat dissipation, the optimization objective Y is selected as the maximum temperature of the battery module, the maximum temperature difference of the battery, and the pressure drop of the liquid cooling plate. Substituting the data obtained in step 8 into the objective function can solve for the coefficients of the objective function.

[0086] In some embodiments of the present invention, the prediction rate of this objective function is greater than 95%.

[0087] Step 10: Obtain the optimization result of the design variables according to the total objective function.

[0088] The expression of the total objective function is:

[0089]

[0090] In the formula, Z is the total objective, Y iis the objective function for the i-th optimization objective, N is the number of optimization objectives, and K i is the optimization objective weighting coefficient.

[0091] In some embodiments of the present invention, the optimization objective Y is selected as the highest temperature of the battery module, the maximum temperature difference of the battery, and the pressure drop of the liquid cooling plate. The total objective function is:

[0092] Z = AY1 + BY2 + CY3

[0093] where Z is the total objective, Y1, Y2, and Y3 are the objective functions of the pressure drop of the liquid cooling plate, the highest temperature of the battery module, and the maximum temperature difference in Step 9 respectively, and A, B, and C are the weighting coefficients.

[0094] Determine the weighting coefficients of the three optimization objectives according to experience and the actual situation of the battery pack system. The weighting coefficient value of the highest temperature index of the battery module is 0.5, the weighting coefficient value of the maximum temperature difference of the battery module is 0.49, and the weighting coefficient value of the pressure drop of the liquid cooling plate is 0.01.

[0095] The optimization results of the obtained optimization variables are: the number of main channels is 7, the number of sub-channels is 10, and the inclination angle of the sub-channels is 75°.

[0096] In some embodiments of the present invention, a computer device is further provided. The device includes a processor and a memory. The memory is used to store instructions or computer programs, and the processor is used to execute the instructions or computer programs in the memory so that the device executes the steps of the method described in the foregoing embodiments.

[0097] In some embodiments of the present invention, a computer-readable storage medium is further provided. Instructions are stored in the computer-readable storage medium. When the instructions run on a device, the device is caused to execute the steps of the method described in the foregoing embodiments.

[0098] In some of the foregoing embodiments of the present invention, the influence between various design variables is considered, a sensitivity analysis is performed on multiple factors (design variables) affecting the pressure drop of the liquid cooling plate, the highest temperature of the battery, and the maximum temperature difference, and multi-objective optimization is performed on the three design variables with greater influence, so that the optimal values of the design variables can be obtained, that is, the optimal structure of the liquid cooling plate can be obtained. Moreover, the heat dissipation effect of the optimized liquid cooling plate is good. Compared with the initial structure, the highest temperature and the maximum temperature difference of the battery pack can be effectively reduced, and the risk of battery overheating can be reduced through precise temperature control.

[0099] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An optimization method for the heat dissipation of an automotive power battery pack based on a liquid cooling plate, characterized in that, Including the following steps: Obtain a three-dimensional model of a battery pack including a liquid cooling plate and a battery module; Establish a finite element model of the battery pack including the liquid cooling plate and the battery module, and perform simulation on the battery pack; Obtain design variables and determine the optimization objectives; Reconstruct a parametric geometric model of the liquid cooling plate structure according to the design variables; Adopt the method of controlling variables, sequentially change the values of each design variable, re-establish a finite element model of the battery pack including the liquid cooling plate and the battery module, and perform corresponding simulations; Select a preset number of design variables based on the influence of each design variable on the simulation results, determine the value range of the selected design variables, and extract multiple groups of sample points; Establish corresponding parametric geometric models and finite element models of the battery pack according to the sample points, perform simulation calculations, and obtain simulation calculation results; Establish an objective function of the design variables and the optimization objectives according to the simulation calculation results; Optimize the selected design variables based on the objective function and the overall optimization objective to obtain the optimization results of the design variables.

2. The optimized method for heat dissipation of an automotive power battery pack based on a liquid cooling plate according to claim 1, characterized in that, Conduct a heat dissipation condition experiment on the battery pack to obtain experimental data; compare the simulation results of the finite element model of the battery pack including the liquid cooling plate and the battery module with the experimental data to make the error between the two within a preset range, otherwise, change the parameters of the finite element model of the battery pack including the liquid cooling plate and the battery module and re-perform simulation calculations.

3. An optimization method for the heat dissipation of an automotive power battery pack based on a liquid cooling plate according to claim 1, characterized in that, The design variables are selected from the height and width of the liquid cooling plate flow channel, the thickness of the liquid cooling plate, the coolant flow rate and velocity, the coolant temperature, the inclination angle of the secondary flow channel, the number of main flow channels, the number of secondary flow channels, and the contact method between the liquid cooling plate and the battery cell.

4. An optimization method for the heat dissipation of an automotive power battery pack based on a liquid cooling plate according to claim 1, characterized in that, After selecting a preset number of design variables, use the experimental design method to extract multiple groups of sample points within the value range of the design variables.

5. An optimization method for the heat dissipation of an automotive power battery pack based on a liquid cooling plate according to claim 4, characterized in that, The experimental design method is one of the central composite design method and the fractional factorial experimental design method.

6. An optimization method for the heat dissipation of an automotive power battery pack based on a liquid cooling plate, according to any one of claims 1-5, characterized in that The expression of the objective function is: where Y is the optimization objective, and x i is the design variable, k i and k mn are coefficients, n is the number of design variables, m ∈ n, k ∈ n, and m ≠ k.

7. An optimization method for heat dissipation of an automotive power battery pack based on a liquid cooling plate according to claim 6, characterized in that Solve the objective function by the mathematical regression method.

8. An optimization method for the heat dissipation of an automotive power battery pack based on a liquid cooling plate according to claim 6, characterized in that, The expression of the overall optimization objective is: where Z is the total objective, and Y i is the objective function of the i-th optimization objective, N is the number of optimization objectives, and K i is the weighting coefficient of the optimization objective.

9. A computer device, characterized in that, The device includes a processor and a memory. The memory is used to store instructions or computer programs. The processor is used to execute the instructions or computer programs in the memory so that the device executes the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, Instructions are stored in the computer-readable storage medium. When the instructions run on the device, the device executes the steps of the method according to any one of claims 1-8.

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